JP2001195594A - Iris identifying system and method of identifying person by iris recognition - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a system and a method for identifying iris, by which the identity of a living animal is identified by iris scan. SOLUTION: An iris image pickup unit is operated by a control unit interfaced with a data processing unit for preprocessing an input image signal into processing data. The processed data express one of plural parameters for iris identification selected from the group of the density and texture style of an iris fiber structure using a frequency converting method (1), pupil reaction (2), form of the iris (3), autonomic nervous loop reaction (4), form of autonomic nervous loop (5), presence of a recess (6), position of the recess (7), and form of the recess (8). The control unit compares the processed data with a parameter previously stored in a data base corresponding to the processed data parameter and can be operated to measure coincidence expressing the confirmation of the identity.

Description

DETAILED DESCRIPTION OF THE INVENTION

TECHNICAL FIELD The present invention relates to an iris identification system and a method for confirming the identity of a living body by using an iris image.
More specifically, the present invention measures the iris fiber structure, the shape of the autonomic nerve ring (ANW) and the pupil and its spontaneous response to light, and one or more identifying parameters relating to the presence, shape and location of indentations from the iris image. Iris identification method.

BACKGROUND OF THE INVENTION Known iris identification systems for identifying a particular person include an iris from an image signal of an iris portion taken from the human eye to allow or reject the particular person according to the results of the comparison. Compare the code with the corresponding iris information retrieved from the database. This conventional iris identification system, however, has not reached an acceptable high level of identification accuracy.

In addition, various iris identification systems have limitations in identifying whether a living human is observing the true iris, thus increasing the risk of falsely identifying a fabricated iris image. Therefore, it cannot be used safely with systems such as banking systems, electronic settlement systems, and the like.

SUMMARY OF THE INVENTION It is an object of the present invention to ascertain the identity of an individual using an iris recognition system having a high level of accuracy. Another goal is to analyze a number of properties related to the iris,
Achieving a high level of identification accuracy.

Accordingly, one object of the present invention is to provide an iris image, light-induced pupil and autonomic ring reactions, autonomic nervous ring and pupil shapes, and the presence of depressions.
It is to quickly and unambiguously identify a living iris by measuring a plurality of iris identification parameters according to the iris fiber structure that can be obtained from the position and shape.

In accordance with the present invention, a system for confirming the identity of a living animal by iris scanning comprises a control unit for receiving identification information identifying the living animal, and
A data storage unit including a database accessed by the control unit for containing predetermined personal information relating to the identifiable organism for comparison with the identification information. The iris image pickup unit including the camera is operated by the control unit to first capture the iris image and generate an input image signal when the identification information corresponds to the predetermined information. The data processing unit pre-processes the input image signal into processed data. For each identifiable living body, the storage unit comprises: (1) density and texture morphology of the iris fiber structure using the frequency conversion method; (2) pupil response; (3) pupil shape; , (5)
A plurality of pre-stored parameters for iris identification, selected from the group consisting of the shape of the autonomic nerve ring, (6) the presence of the depression, (7) the position of the depression, and (8) the shape of the depression. Including at least one of them. The control unit may be operable to compare the processed data with the parameters to determine whether there is a match indicating that the identity has been confirmed.

According to a preferred aspect of the present invention, the camera is configured to photograph both eyes of a living animal. The data processing unit separately processes the image input signal into processing data representing each eye. In this way, a higher level of identification accuracy is obtained by iris scanning to confirm the identity of the surviving animal.

[0008] In order to access the identification system, the organism to be identified first inputs identification information into the system. In some embodiments, the identification information data can be entered using a card reading unit, key entry unit, etc., that accesses the system using a PIN (Personal Identification Number) or some other unique identification information. . Also,
A voice activation system can also be used.

[0009] The iris image pickup unit is preferably operated by the control unit in conjunction with the camera and includes a light source for continuously capturing a plurality of iris images and generating an input image signal. By illuminating the eye, it is noted that the iris contracts, and therefore a continuous image allows the system to capture the iris contraction and, if desired, the subsequent iris dilation. The light source is preferably an infrared light source.

In accordance with another unique feature of the present invention, a system for controlling a camera based on accessing predetermined personal information in a database containing characteristics of a user's physique translated into positioning data. It has an interface between the iris image pickup unit and the control unit to enable automatic alignment with the user's eyes.

[0011] As noted above, the system preferably has the ability to analyze different parameters that can be attributed to iris characteristics. However, the present invention
By screening and analyzing only one of the above described process parameters or a subset of the process parameters below the entire parameter set, one can operate to confirm the identity of the user.

[0012] The unique method used to analyze the density and texture morphology of the iris fiber structure preferably comprises a frequency conversion method using the Haar transform. According to a presently preferred aspect, the present invention provides a variable multi-sector system for spectral transformation using a Haar transform function. According to this unique method, a selected one of the iris images is divided into a plurality of sectors and a number of Haar function coefficients are calculated for each sector. This multi-sector system of spectral analysis advantageously makes it possible to eliminate sectors with irises that are distorted by interference caused by the presence of defects or interferences, for example by eyelids, eyelashes and the like. These deficiencies are manifested by the generation of high frequency coefficients and predetermined sharp variations between adjacent sector coefficients. Therefore, the analysis for each adjacent sector makes it possible to apply the Haar transform to the iris recognition technology.

Using the Haar transform, it is possible to produce an iris reference record with an unprecedented high level of recognition accuracy, preferably in combination with selecting Haar coefficients representing the selected low frequency zone. .

The invention, in one aspect, uses a series of iris images to generate a pupil histogram or pupillogram represented by a curve representing iris contraction and dilation over time. The present invention provides a novel technique for analyzing pupilograms by analyzing the gradient change as a further means to improve identification accuracy. The present invention also provides
As a further means for improving identification accuracy, the pupil boundary, shape and position are analyzed.

The present invention also processes information about the shape, response, and, if present, the presence, location, and shape of the indentation, as a further measure to improve identification accuracy.

The present invention also contemplates and enables the analysis of the iris identification parameters described above for the system, which is used for purposes other than iris identification. For example, it is within the scope of the present invention to form a system for testing an individual for the presence or use of a drug or alcohol. In this type of system, a control unit for receiving identification information identifying a living animal, and a control unit for including predetermined personal information relating to an organism that can be identified for comparison with the identification information. A data storage unit is provided that includes a database accessed by the unit. The iris image pickup unit includes a camera scanned by a control unit for capturing an iris image and generating an input image signal. The data processing unit pre-processes the input image signal into processed data. The control unit processes data resulting from processing of the input image signal representing the average contraction and dilation of the iris pupil in response to light stimuli per hour to determine whether the processed data indicates current or past addiction. Measure. This system is based on the finding that the shape of the curve of the pupilogram is different when a person is addicted to alcohol or drugs compared to the shape of a curve when a person does not contain alcohol or drugs.

[0017] Methods for confirming the identity of living animals or testing for the presence of at least one of drugs and alcohols in the human body also require special selection of iris identification parameters as described above. On the basis of the,
Provided by the present invention. Still other objects and advantages of the present invention will become apparent to those skilled in the art from the following detailed description. In the following description, only the preferred embodiments of the invention are shown and described merely as indicative of the best mode intended to practice the invention. As will be realized, the invention is capable of other different aspects, and its several details are capable of modifications in various obvious features, all without departing from the invention. Accordingly, the drawings and description should be regarded as illustrative and not restrictive.

DETAILED DESCRIPTION OF THE DRAWINGS FIG. 1A is a block diagram of a system 14 for identifying an iris of a living subject according to the present invention. Living organisms include living animals having an iris for identification, including humans and animals. The system 14 preferably includes a card reading unit 40 for reading and identifying information recorded on the card storing the personal card number by accessing an information recording medium of the card.
A key input unit 50 having a plurality of keys for inputting a PIN generates an electric signal corresponding to each key input. Iris image pickup unit 20
Flashes automatically and continuously according to a predetermined order of a specific control signal, and a camera having an auto-focus function is operated for a few seconds (for example, at a speed of generating 25 frames or more per second). It has multiple light sources that allow capturing a dynamic image of the iris. Drive unit 3
0 supplies power for illumination and adjustment of the camera position to the iris image pickup unit 20 by supplying specific control signals using a motor.

The data processing unit 60 pre-processes the input image signal captured by the iris image pickup unit 20. Data storage unit 80
Are associated with each person's personal card number or PIN, respectively: (1) the density and texture form of the iris fiber structure that can be extracted from the individual iris image signals;
(2) movement of the autonomic nerve ring (ANW) in response to light;
(3) Pupil movement in response to light; (4) ANW shape;
A database for storing a plurality of identification parameters including (5) pupil shape; (6) presence, position, and shape of the depression is formed. The control unit 70 searches the database for the card number or PIN entered or read by the card reading unit or key input unit, obtains a dynamic iris image, and automatically positions the camera based on personal information. Identifying the user's identity by adjusting and measuring a plurality of iris identification parameters to determine the user's identity from the image signal identifies the iris for a particular human. In this case, in addition to the iris information about a particular person, further information about each person (eg, camera height when capturing an iris image,
Alternatively, the individual's height information, which is directly related to the most appropriate camera height for each person, is stored in the data storage unit 80, and the unit 80 automatically adjusts the camera height by reading the additional information. Can be made possible.

FIG. 1B is a side perspective view of a portion of the binocular camera and illumination system showing an example of the iris image pickup unit 20 of FIG. 1A. The iris image pickup unit 20 has a camera 16 and a plurality of first and second lenses 11 and 12 at positions required to capture an image of the iris. It is preferable to use a camera 16 having a variable capture zone (flex zone) for autofocus. Further, it is preferable to use a second lens 12 having a depth of focus greater than 5 mm to prevent a reduction in the autofocus function as a result of eyelid and eyelash movement and blinking of the user. The first and second lenses 11, 12 are preferably provided for each eye.

Iris image pickup unit 20
Further, in connection with the iris image pickup unit 20 of FIG. 1B, the visual field guiding unit 18 (FIGS. 2 to 2) for fixing the user's eyes to the central portion of the camera to prevent the iris from being biased to one side. (See FIG. 4). Further, in some embodiments, a side light source for initial autofocus and brightness adjustment can be used. FIG. 2 is a reference diagram from FIG. 1B to show the view pointer, and FIG. 3 shows the view pointer of FIG. 2 in detail. FIG. 4 is a reference diagram for explaining the operation of the visual field pointer in FIG. 2 to reflect light from the visual field guiding light source L4 to the user's eyes.

As shown in FIGS. 2 to 4, the visual field guiding unit includes a guiding light source L4 (reference numeral 17) that emits light blue or red light for guiding a user's visual field.
As shown). The reflection unit 18b reflects the light from the light source 17 from the central portion of the camera 16, and the support unit 18a aligns and supports the reflection unit at the center of the lens. In this case, the support unit 18a is preferably made of a transparent glass-like material so as to prevent affecting the iris image even when located at the center of the lens. The reflection unit 18b has a reflection surface having a certain inclination so as to reflect light from the visual field guide light source L4 to the user's eyes.

FIG. 5 is a front view of the second lens 12 of FIG. 1B. In one arrangement of the front surface 12 of the second lens, a flash light source L3 (12b) is provided that stimulates pupil and ANW movement (ie, contraction and dilation). A plurality of infrared light sources L2 (12a) are arranged in an annular or annular array along the periphery of the lens 12 so that the software allows the camera 16 to capture an iris image and a clear pupil image. Is controlled.
Flash light source 12b is preferably a blue diode that is controlled by software to automatically emit light in short cycles.

FIG. 6 is a flow chart of an example of taking in an image signal by the iris image pickup unit 20 performed by computer control. In adjustment step S61, the camera position (height) is adjusted by using personal information retrieved from a database based on the card number or PIN, and the plurality of light sources (12a and 17) are simultaneously set for autofocus. Switched to ON, attracts the user's eyes to the objective lens. In step S62, the user's eyes are guided to the objective lens by using the light source 17 that emits light. Camera auto focus is step S
At 63 is performed. In step S67,
A light source 1 in a short cycle in which a moving ANW and pupil dynamic image induces iris pupil and ANW movement.
Acquired for several seconds by the emission of 2b (S66). In step S68, the above-described steps S61 to S68
After, all emitting light sources are turned off.

FIG. 7 is a detailed flowchart of the adjustment of the camera height in step S61 of FIG. The flowchart includes the following steps: Enter a card number or PIN (S71); obtain information on the camera height of the corresponding individual by searching the database for the entered card number or PIN (S72). ); After comparing the acquired information of the camera height with the information of the current height of the camera, adjust the camera height suitable for the corresponding individual by rotating the motor forward or backward (S73). I do.

FIG. 8 shows a selection zone (x) of the input iris signal required to perform iris analysis according to one aspect of the present invention.
5 is an example of an iris image for explaining the following. The zone to be selected for analyzing the iris must include at least a portion of the ANW 10b and the entire pupil 10a. Further, the selection zone (x) is preferably a portion that is clearly visible and is not affected by eyelids, eyelashes, and the like, and is set within a range of more than １ ／ and less than の of the iris diameter.

FIG. 16 is an illustration of iris image analysis using a variable multi-sector system of spectral analysis in a preferred embodiment of the present invention, as described in more detail below. The variable multi-sector system of spectral analysis allows the selection of the visible portion of the iris to be distorted by interference for analysis, thereby improving the reliability of the identification process. Anatomical features of the eye caliber and eyelid bulge can be identified and eliminated. Edge defects or interferences, or glare, which are almost impossible in advance, can be identified and eliminated.

FIG. 9 is a high-level flowchart for explaining the whole iris identification method of the present invention.
C is a detailed series of flowcharts for the steps of the high-level flowchart of FIG.

As shown in FIGS. 10A to 10C, in the iris identification method according to the present invention, various aspects can be recognized by including one or more of the following processes. First, the identity of a user can be confirmed by searching and acquiring personal information according to a corresponding number from a database by a card number or PIN confirmation process S101 to S103. After entering the card number or PIN, the image acquisition process S1
According to 04-S105, a dynamic iris image is acquired by adjusting the camera position according to the user's personal information obtained during the confirmation process and controlling the plurality of light sources.

The different identification parameters that can be obtained and analyzed according to the invention are described below.

Density and Texture Morphology of Iris Fiber Structure Using the iris fiber structure confirmation processes S106-S110, the image signals of the zones suitable for iris analysis by Wavelet transform of the selected still iris image acquired during the above process. After measuring the identification parameters by transforming, the identity of the user is ascertained by the corresponding identification parameters. The transformation can be applied to the zones of FIG. 8 or to the individual sectors of FIG. 16, as described below with respect to the variable multi-sector system of spectral analysis.

In one embodiment of the present invention, the iris structure confirmation process includes steps S106 and S107 (FIG. 10A).
, By pre-processing the dynamic signal of the iris image, a zone suitable for iris image analysis (selection zone x; substantially greater than one-third of the iris diameter, including part of the ANW and the entire pupil). / 2) (in a range smaller than / 2); in step S108, two-dimensional Wave
et transformation (eg, Wavelet like Haar transformation)
Wave which represents specialized information on iris fiber structure density and texture morphology after transforming the selected still image signal of the selected zone by the t transform).
Measure the et conversion coefficient identification parameter; Step S10
9. In S110, the identity of the user is confirmed by searching the corresponding parameter from the database;

Wavelet Transform Generally, the density and texture morphology of the iris fiber structure varies from person to person. Wavelet transforms containing information about the density mainly in the low frequency zone (especially Haar
In particular, when performing Wavelet transformation such as transformation,
The low-frequency component of the two-dimensional Haar transform coefficient has most information on the iris fiber density and texture morphology.

The low frequency component is subjected to Wavelet transform on the iris image signal selected as described above, and the frequency characteristic is measured.
By determining the selected low frequency coefficient of the ar transform as an identification parameter, it is selected to have most information on iris fiber density and texture morphology. The Haar transform so applied in the present invention uses the following Haar function which is symmetric, separable, unitary and varies with size (scale) and position.

A further Haar function is defined as follows:

(Equation 1)

To simplify the spectral analysis algorithm, the preferred embodiment uses a standardization factor of 2- ^{m / 2}
Ignore Therefore, the definition of the above function is as follows.

(Equation 2)

This function is also called the Signum Haar function. The binary of the function determines the order of the function in the listing. In the present invention, when measuring the actual Haar transform, a huge amount of calculation is required for a large-sized image. However, the computation time is greatly improved by the butterfly structure of the transformation flow graph.

FIG. 15 shows a graph of a rapid spectrum conversion algorithm using the Signum Haar function (here, n = 18). This graph represents the butterfly structure of the conversion flow graph. Along the vertical axis of the graph, P0 to P7 indicate pixel values to be converted.

In this flow graph, the value for each continuous level that progresses to the right is based on the previous value based on the following equation.

(Equation 3) Where m = n + 1 and X [n] is the brightness of pixel n.

As a result of using the butterfly structure of the transformation flow graph, the spectrum transformation program with the Haar function operates 200 times faster than the conventional method.

Variable Multi-Sector Analysis A variable multi-sector system for spectral analysis using the Haar function will be described below. The variable multi-sector system is preferably implemented throughout the zone selection method described above with respect to FIG. Occasionally, glare formation, interference in the eyelids and eyelashes occurs within the section of the iris,
Alternatively, a large visible portion of the iris may not be available for analysis. The interference or unavailability of the iris portion reduces the number of sectors available for comparison, thereby reducing the reliability of human identification.

In the image analysis, the iris image is converted to the image shown in FIG.
As shown in, the inner ring zone including the outer ring 100 and the pupil 120 is also started by splitting. The boundary separating the outer inner and outer rings 100 from the annular zone is:
The distance is set to 50 pixels from the origin of the iris. 50
Values larger or smaller than pixels can be used in further aspects. The outer border of the outer annulus begins approximately at the sclera-iris border. The diameter of the outer annulus can vary due to many factors, such as different iris sizes between individuals, different ranges for obtaining iris images, and the like. Iris corresponding to outer ring 100 and pupil 1
Since region 110 between 20 and outer ring 100 contracts and expands at different rates, different normalization factors are used for the inner and outer rings.

In a currently preferred embodiment, each region 100, 110 is then divided into 16 sectors, preferably of equal size, ie, the reference symbols I ₀ -I _{31 in} total.
Is further subdivided into a total of 32 sectors. For each of the 32 sectors I _{0 to} I ₃₁ , 1,024 using the Wavelet transform as described above
The Haar function coefficients are calculated to form a 32 × 32 matrix. However, the total number of sectors in all zones (n + p), the number of sectors in each individual zone (n, p), the dimensions of the sectors and Ha
It is within the scope of the present invention that the number of ar function coefficients may be varied. That is, each zone may have a different number of sectors (n, p), ie, the region 11
0 can be radially divided into 10 sectors (P = 10), while outer ring 100 is radially divided into 16 sectors (n = 16).

Next, generally, sectors that are obstructed or interfered by eyelids or eyelashes, indicated by reference symbols 13A and 13B with respect to the lower and upper eyelids, respectively, are rejected by adjacent sector coefficient comparison. If a sharp change in the high frequency coefficient is observed by comparing adjacent sector coefficients, the sector is rejected as a defect. Referring to FIG. 16, the sector comparison is:
This is done for a group of four sectors starting at the horizontal which divides sector 0 from 15 and sector 7 from 8 and proceeds clockwise or counterclockwise for adjacent sectors. In this method, sector I
_{0 to} I ₃ , I _{7 to} I ₄ , I _{8 to} I ₁₁ and I _{15 to} I
₁₂ are compared.

For example, the high frequency coefficients in sectors I ₀ and I ₁ are compared, and if the difference does not exceed a predetermined sector high frequency coefficient threshold,
₁ is recognized as good. Sector I ₀ is recognized as good or rejected during pupil analysis if closed by eyelids. Next, the coefficient of sectors I ₁ and I ₂ are compared, as shown in FIG. 16, the border of eyelid is located in sector I _2. Boundary in the sector I ₂ will cause the difference exceeding the sector high frequency coefficient threshold and sector I ₂ will be rejected. After rejecting the sector I _2, sector I ₃ also be rejected as well. A similar process is repeated for the remaining quadrant sectors.

After removing the rejected sectors from the analysis, a subset of the complete 1,024 Haar coefficients is selected. The number of coefficients selected is determined by various factors. Unnecessarily too many coefficients increase the size of the database, while too few coefficients degrade recognition quality. In addition, some coefficients are not selected because they change with changes in image brightness, and some high frequency coefficients contain too much noise. As a result of the experiment, a preferable mode is 32 × 1,024 Haar coefficient.
31 coefficients selected from 32 matrices are used. The specific matrix positions of the selected coefficients are: (0,1), (1,0), (1,1), (0,
2), (0,3), (2,0), (3,0), (1,
2), (1,3), (2,1), (3,1), (0,
4), (0,5), (0,6), (0,7), (4,
0), (5, 0), (6, 0), (7, 0), (1,
4), (1,5), (1,6), (1,7), (4,
1), (5,1), (6,1), (7,1), (2,1)
2), (3,2), (2,3) and (3,3). More or less coefficients can be used in different embodiments.

By selecting 31 coefficients from each of the 32 sectors I ₀ -I ₃₁ , an iris reference record approximately 1 kilobyte in size can be generated. The first 32 bits of the record contain the result of the sector rejection analysis.

The new iris image that is identified is processed in a manner similar to the reference image described above. The resulting characteristic record is compared to all iris reference records recorded in the database. The sum of the difference between the input and the reference iris coefficient is calculated for each sector. The value of the sum is in the range of 0 to 2 for coefficient normalization. A total value of 1 represents an absolutely gray image, 0 represents a perfect match of the sector spectrum, and 2 represents a spectrum of equivalent modules but of opposite sign.

Direct experiments have shown that some sectors differ from the reference sector due to iris peristalsis (rapid unconscious movement of some areas of the iris).
Therefore, only sectors with a total value less than 1 are preferably used in iris image analysis. In this way, the sectors perturbed by peristalsis are added to the rejected sectors and excluded from the recognition task. The minimum number of sectors used in iris image analysis is 10 for each eye. Fewer sectors can be used if the quality of perception is correspondingly reduced. Multiply the results of the sector coefficient comparisons to emphasize the differences. Thus, if the image to be identified matches the reference image, the value obtained will be close to zero. On the other hand, different images lead to hundreds of values.
Within the thousands.

Since the low frequency spectral coefficients contain a lot of information about the density and texture morphology of the iris fiber structure, the coefficients of the low frequency zones experimentally selected from the results of the Haar transform are used for identification. .

Autonomic Nervous Ring and Pupillary Response Pupil and ANW Response Confirmation Process S111-FIG. 10B
At S116, by detecting the pupil and the ANW from the acquired dynamic image of the iris, the identity of the user is confirmed by the corresponding identification parameters. Next, the pupil and ANW from the detected dynamic image signal
The identification parameters are measured using the dynamic response (contraction and expansion) of

Further, in one aspect of the present invention, the pupil and ANW response confirmation process can comprise the following steps: Pupil zone by computer processing a central portion of the acquired dynamic image of the iris. (S111, S112 in FIG. 10B); computer processing of the ANW zone (S113); response of the moving pupil and ANW in the detected zone (expansion or contraction).
After computing the time, the surviving pupil and ANW
The identification parameter for recognizing each is measured (S
114); Verify the identity of the user by searching the database for the corresponding parameters.

FIG. 11 shows the pupil 10 at a constant angle θ.
a and an iris 10c for explaining the movement of the ANW 10b
FIG. 12 is a graph showing the average pupil radius R1, the pupil radius R2 at a certain angle θ, and the ANW radius R3 at a certain angle θ converted in one-dimensional data with time variation. is there. In the graph, the first time “t1” represents the operation time of the flash light source, the second time “t2” represents the start time of the contraction of R1, and the third time “t3” represents the time when the average pupil radius is the minimum. Represents the time, and the fourth time “t4” is a time when the pupil radius is a constant angle θ.
Represents the minimum time, and the fifth time “t5” is AN
The radius of W represents the minimum time at a constant angle θ. Therefore, as a result of comparing these dynamic images, it is possible to obtain respective identification parameters through the pupil and the ANW that respond unconsciously to light.

For this purpose, the present invention provides a predetermined method which is considered to represent a living body if the movement of the pupil radius exceeds a certain percentage (for example, 5% or more) when the pupil contracts. Use the reference value. Pupil edges must also be detected from the captured iris dynamic image to observe pupil movement. At present, it is possible to detect the pupil edge after determining the approximate center of the iris by using a symmetric center search algorithm.

According to this method, even if the iris image cannot be captured at its center and is somewhat shifted to the right or left, the iris can be identified without causing an error. .

If the iris image is too biased for identification, it can be captured again. In addition, when another image is captured instead of the iris image, a true image and a false image can be distinguished in many cases.

In the symmetric center search algorithm described above, the following function F (i) is obtained for the horizontal and vertical lines of the image.

(Equation 4) here,

(Equation 5) And N are the lengths of the image lines, x (i) is the luminance of the ith pixel of the horizontal or vertical line, and if i ≦ 0, then x (i) = x (0), i When ≧ 0, x (i) = x (N).

In such a case, the definition domain (i) that minimizes the absolute value of the function F (i) exists at the center of symmetry. Providing these methods for horizontal and vertical lines, the point of intersection of the defined domain (i) that minimizes the absolute value of the function F (i) is set as the center of symmetry. If the domain (i) of the definition of the horizontal and vertical lines scatters without traversing, especially if they deviate from a predetermined range, this means that the captured image is not an iris image or an iris image Indicates that the iris is excessively biased to the right or left, and thus the iris image must be recaptured before further identification can be performed.

If a dynamic image was captured for the iris, the pupil and ANW would be
It is contracted and expanded by 2b. Pupil and AN
The movement of W does not occur at the same time, but shows different types for each person. The parameters from the pupil and ANW response (FIGS. 11 and 12) resulting from such non-simultaneous movements are used for identification.

Referring to FIG. 17, pupilogram or
By the pupil histogram, the time T ₀Hula of light in
The average contraction and dilation of the pupil in response to ash is shown.
The vertical axis indicates the size or radius of the pupil, and the horizontal axis indicates T₀Or light
Indicates the time that begins by the time of the flash. T
_LIs when the pupil begins to contract in response to a flash of light
Between; T_L-T₀Is the latency of the pupil response.
A_LIs the average radius of the pupil before the flash of light. T
_mIs the minimum radius A of the pupil_mIs the time of contraction. Pupil
The value obtained by subtracting the latent time from the time to shrink is T_m-T
_LOr T_PIt is. T₁And T₂Are each dynamic rainbow
The 40th and 70th times of the Aya image₁
And A₂Is the time T₁And T₂Corresponding pupil at
Radius. S_PIs T_LAnd T_mSpeed of pupil contraction between
Degrees. D_ABIs T_LAnd T_mPupil contraction curve between
It is a straight line distance along. % A₁Is T₁Pupil in
Expansion distance or A₁-A_mAnd average pupil contraction distance or A_L
-A_mAnd the ratio. % A₂Is T₂Pupil dilation in children
Distance or A₂-A_mAnd average pupil contraction distance or A_L-A_m
And the ratio.

The pupilogram of humans using drugs or alcohols is different from the pupilograms of humans not using drugs or alcohols.
Pupilograms can be used to detect drug and / or alcohol use by individuals. In a human pupilogram using a drug, the time for the pupil to contract to the minimum radius T _M is slower than the standard time. Pupilograms for humans using alcohol are flatter, ie, the D _AB is smaller for alcohol users compared to humans who do not use alcohol. Moreover,% _{A 1} and% _{A 2} are smaller with respect to the alcohol user.

Autonomic Nervous Ring and Pupil Shape Next, ANW and pupil shape confirmation processes S117 to S1
According to 19, the identity of the user is ascertained by detecting the ANW and the pupil from the selected iris image and measuring the identification parameters according to the detected shape of the ANW and the pupil. The ANW shape is uniquely measured in the present invention by using a central axis transform applied to the selected iris image signal. Pupil shape is uniquely measured in the present invention by using edge detection and curve matching algorithms, as is known in the art.

Autonomic Nerve Ring In addition, in each aspect of the present invention, the ANW and pupil identification process may comprise the following steps: The iris image signal selected by step S117 (FIGS. 10B and 10C). , The ANW and the pupil are detected from step S118; the two-dimensional image signal of the detected ANW is transformed by the central axis transformation, and the pupil shape is transformed by the edge detection and curve matching algorithm as described above. Computer calculation of identification parameters based on the shape of
According to 119 and S120, the identity of the user is confirmed by searching the corresponding identification parameter from the database.

FIG. 13 is a view showing an example of the ANW 10b and the depression 10d representing the edge detected from the iris image.
FIG. 14 is an exemplary diagram of the ANW 10b and the depression 10d shown in FIG. 13 with the one-dimensional data converted by the central axis conversion. The center axis transformation of an object having a specific shape is, for example, the position of the center of a circle that touches the object boundary at two or more points. In other words, this is the position of the point closest to the target boundary. Therefore,
Since it is possible to transform a two-dimensional object into one-dimensional data by means of a central axis transformation, this can be applied to identify the shape and position of the ANW shape and the pupil.

FIG. 18 is an illustration of the pupil and iris showing the shape, position and identification parameters. Pupil shape is uniquely measured in the present invention using edge detection algorithms known in the art. Pupil edges are generally
In FIG. 18, it is indicated by reference numeral 10a. Further, the centers of the iris and pupil, indicated by reference signs 10i and 10h in FIG. 18, respectively, are measured by geometry and used as identification parameters.
The separation direction and distance between the center of the iris and the center of the pupil, generally indicated by reference symbol 10j, are used as identification parameters.

Furthermore, many pupils are not perfectly circular in shape, but many are ellipses that deviate in the direction of the major axis of the ellipse. In a preferred embodiment, the orientation and magnitude of the pupil ellipticity is uniquely measured in the present invention using curve and shape matching algorithms known in the art. Referring to FIG. 18, the major axis and the minor axis of the pupil ellipse are
Indicated by reference symbols 10m and 10n, respectively. The pupil ellipticity is expressed as the ratio between the short axis 10n and the long axis 10m. Recognize and characterize the flat portion along the periphery of the pupil, generally indicated by reference numeral 10f in FIG. 18, and the area where the pupil is depressed or bulging, indicated by 10g and 10e, respectively, and recognize them as further identification parameters. Quality can be improved.

Detection of dents Further, using dent confirmation processes S121 to S127, it is determined whether or not dents are present. If a dent is present, the zone is detected. The identity of the user is ascertained by measuring identification parameters based on the location and shape of the depression.

Further, in the iris identification method according to the present invention, the indentation confirmation process can be applied to the above-described embodiment by including at least one of the following plurality of steps shown in FIG. 10C. That is, a first dent confirmation process S121 is performed, and then a step S12 is performed.
5 to S129, verify the identity of the user by determining whether a depression is present based on the selected still image of the acquired iris,
If a dent exists, the image signal of the dent is converted by central axis conversion, and the identification parameter is measured based on the position and shape of the dent from the conversion result. Second
The indentation confirmation processes S122 to S124 are performed when the indentations do not exist as a result of the first indentation confirmation process. If not, the user is allowed; conversely, if there is a depression in the reference iris, the user is rejected.

Further, in each aspect of the present invention, the first dent confirmation process can comprise the following steps: Step S121 (FIG. 10C), from the acquired iris still image signal, the iris. It is determined whether an indentation exists by detecting an edge of the image; if an indentation exists as a result of the determination step, an indentation zone is detected in step S125; and an indentation zone is detected in step S126. After converting the two-dimensional image signal of the hollow zone by the central axis conversion, the identification parameters are computer-calculated based on the shape and position of the hollow; steps S127 to S12.
9 ascertains the user's identity by searching the database for the corresponding parameters.

Further, in each aspect of the present invention, the second indentation confirmation process can comprise the following steps: Step S121 determines whether an indentation is present in the input image. If it is determined in step S122 that no depression is present in the input image, it is determined again whether or not a depression is present in the reference iris; if it is determined in step S123 that no depression is present in the reference iris, Is permitted, and rejected if there is a depression in the reference iris in step S124.

The operation of the present invention configured as described above and the effects achieved by the present invention will be described below by using examples.

First, the card number is read by the card reading unit 40 or the key input unit 50,
Alternatively, the user's PIN is input by keystroke.
A card number and PIN verification process is performed to determine whether the corresponding number is present by searching for the card number and PIN in the data storage unit 80 in the control unit 70.

If there is no corresponding number, the user is rejected as an unidentified user. If a corresponding number is found, then the control unit 70 allows the user to be identified and then, after reading further personal information corresponding to the card number or PIN, sends the control signal to the motor. It is sent to the drive unit 30 to adjust the camera height to suit the user. At this point, the control signal compares the height difference between the current camera position and the camera position appropriate for the user,
The camera height is then automatically adjusted by driving the motor forward or backward. Further, the width between the two eyes can also be adjusted for each user.

Next, the control user 70 controls the iris image pickup unit 20 to turn on the infrared light source 12a, thereby performing the first autofocus adjustment. The user is guided to the visual field pointer 18 by turning on the visual field guiding light source 17, thereby preventing the iris image from being biased in the position direction. Therefore, when capturing an iris image, the field of view of the captured human iris image depends on the guiding light source 17.
(I.e., light blue light source) fixed to the center of the lens.

The iris dynamic image is taken for a few seconds while operating the flash light source 12b in a short cycle. Then all light sources are turned off. The iris image is obtained by controlling the flash light source 12 controlled in a short time by predetermined software.
b must be taken to indicate the changing shape of the pupil and the ANW. To this end, the present invention takes more than 25 iris images per second.

The continuous image of the iris captured as described above is pre-processed in the data processing unit 60. Next, after selecting a zone suitable for iris analysis, an image signal of the corresponding zone is selected. In a preferred embodiment, the analysis is performed on a sector-by-sector basis according to the variable multi-sector system of spectral transformation described in detail above. In another aspect, the zones used to analyze the iris are horizontal strip-like areas that include the entire pupil as shown in FIG.
That is, it is a part that is clearly recognized. This is because it is unaffected by the identified human eyelashes, eyelids, etc.

The control unit 70 performs an iris fiber structure verification process to verify the identity of the user by using only the selected low frequency components, as described in detail above.

Next, a pupil and ANW response confirmation process is performed in the control unit 70, and after the pupil and ANW responses are analyzed from the pupil dynamic image, the pupil and ANW contraction and expansion are caused by the flash light source 12b. In this case, the image captured as representing the living body is permitted, and if there is no movement, the image captured as not representing the living body is rejected.

Subsequently, an ANW and pupil shape confirmation process is performed by the control unit 70 to convert the two-dimensional image signal of the ANW zone having different characteristics in each human from the selected still iris image into the central axis transformation described above. The identity of the individual is confirmed based on the ANW and the pupil shape by converting it to one-dimensional data by (or glass fire method). Certain features of the pupil as described above are also identified and compared.

Next, the first and second dent confirmation processes are performed by the control unit 70, and the identification method for identifying whether or not the dent is present and, if so, the position and shape of the dent, are performed by the user. Is finally confirmed. As shown in the results of the median axis transformation of the ANW, the dimples were extracted from the selected iris still images of FIGS. 13 and 14 by an image processing process, and if the dimple shape was clearly recognized by edge detection, By expressing the shape and position of the one-dimensional data by the central axis conversion, the shape and position of the depression are used as identification parameters.

Thus, the present invention provides a method for iris fiber structure and texture morphology, pupil and ANW response to light,
By measuring the parameters for multiple iris identification using the ANW and pupil shape, the presence, shape and location of indentations obtained from the iris dynamic image, the iris of a surviving human can be quickly and safely prevented from forgery. Accurate identification provides a way to identify a particular person, thus preventing financial accidents when applied to banking and electronic processing / closing systems, and security when applied to access control systems. It has numerous advantages in that accidents associated with can be eliminated.

[Brief description of the drawings]

FIG. 1A is a block diagram of an exemplary system for identifying an iris of a living body according to the present invention. FIG. 1B is a side perspective view of the camera showing the embodiment of FIG. 1A and one embodiment of the iris image pickup unit in the lighting system.

FIG. 2 is a reference diagram partially taken from FIG. 1B to illustrate a field pointer used for the iris image pickup unit of FIG. 1B.

FIG. 3 is a detailed view of the field pointer of FIG. 2;

FIG. 4 is a reference diagram illustrating the view pointer of FIG. 2 that reflects a view guiding light source to a user's eyes.

FIG. 5 is a front view of a second lens in the system of FIG. 1B.

FIG. 6 is a flowchart of an example in which an iris image pickup unit is controlled by a computer to capture an image signal.

FIG. 7 is a flowchart of an example of height adjustment of the camera in FIG. 6;

FIG. 8 is an exemplary diagram of an iris image for explaining a selected area (x) of an input image signal required for iris analysis.

FIG. 9 is a high-level flowchart illustrating all operations of the iris identification method of the present invention.

10A to 10C are detailed continuous flowcharts of the flowchart of FIG. 9;

FIG. 11 is a view showing an example of an iris image for explaining dynamic movement of the iris and the autonomic nerve ring at a certain angle.

FIG. 12 is a corresponding graph showing the radius of the pupil of FIG. 11 in the form of a pupil histogram or pupilogram and showing the autonomic nerve ring converted to one-dimensional data due to time variation.

FIG. 13 is an exemplary diagram of an autonomic nerve ring and a hollow from which an edge of an iris image is extracted.

FIG. 14 is a view showing an example of the autonomic nervous ring and the hollow of FIG. 13 converted into one-dimensional data by central axis conversion.

FIG. 15 is a diagram of a butterfly structure of a Haar transform flow graph.

FIG. 16 is an exemplary diagram of an iris image for explaining a variable multi-sector system for spectral analysis.

FIG. 17 is a graph of another Pupilogram of the average pupil radius per hour.

FIG. 18 is an exemplary diagram of a pupil including extracted pupil characteristic information.

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) H04N 5/225 H04N 7/18 K 5L096 7/18 A61B 5/10 320Z (31) Priority claim number Patent application Hei 11-350612 (32) Priority date November 5, 1999 (11.5 November 1999) (33) Priority claim country Japan (JP) F-term (reference) 4C038 VA07 VB04 VC01 VC05 5B043 AA01 AA09 BA04 DA06 EA06 EA07 EA08 EA09 EA10 FA03 GA03 5B047 AA23 AB02 BB01 BC04 BC05 BC11 BC16 BC20 CA12 CA17 CA19 CB17 5C022 AA05 AB15 AB22 AC54 AC74 5C054 CA04 CA05 CF05 FC12 HA05 HA18 5L096 AA06 BA08 BA18 CA03 FA14 FA17 FA02 FA04 GA19 HA02 HA08 JA11 KA09

Claims (75)

[Claims] 1. A system for confirming the identity of a living animal by iris scanning, comprising: (a) a control unit for receiving identification information for identifying a living animal; A data storage unit including a database accessed by the control unit for containing predetermined personal information relating to an identifiable organism for comparison; (b) the identification information corresponds to the predetermined information An iris image pickup unit including a camera operated by a control unit to capture an iris image and generate an input image signal, and (c) a data processing unit for pre-processing the input image signal into processed data; Wherein the storage unit comprises: (1) a density and texture of an iris fiber structure using a frequency conversion method; Char form, (2) pupillary reaction, (3) the shape of the pupil, (4) autonomous nervous ring reaction, (5)
For pre-stored iris identification of an identifiable organism, selected from the group consisting of: the shape of the autonomic nerve ring, (6) the presence of a depression, (7) the position of the depression, and (8) the shape of the depression. And wherein the control unit compares the processed data with the parameter to determine whether there is a match indicating that the identity has been confirmed. The system characterized in that the system can be operated. 2. The method according to claim 1, wherein the camera is configured to capture two eyes of the living animal, and wherein the data processing unit separately processes the image input into processed data signals representing each eye. The system of claim 1 operable on a computer. 3. The system according to claim 2, wherein the same parameters are examined for identification purposes for each eye. 4. The system according to claim 2, wherein different parameters are examined for identification purposes for each eye. 5. The system of claim 4, wherein for each eye, different parameters are selected as a function representing the identified characteristic or characteristic associated with that eye. 6. The system according to claim 1, further comprising at least one card reading unit and a key input unit for first receiving the input identification information and sending it to the control unit. 7. The iris image pickup unit is operated by a control unit in conjunction with a camera,
The system of claim 1, including a light source for capturing a plurality of iris images in succession to generate the input image signal. 8. The system according to claim 7, wherein said light source is an infrared light source. 9. The system of claim 1, wherein the predetermined personal information includes positioning data that interfaces with the iris image pickup unit by the control unit to automatically align a camera with a user's eye. . 10. The system of claim 1, wherein the processing data represents at least two of the parameters. 11. The system of claim 10, wherein said processing data represents all of said parameters. 12. The system of claim 1, wherein the frequency conversion method uses a Wavelet transform. 13. The system of claim 12, wherein the frequency conversion method uses a Haar transform. 14. The system of claim 1, wherein the processing data includes a pupil shape measured using at least one of a curve fitting and edge detection algorithm. 15. The system according to claim 14, wherein the processing data includes a pupil ellipticity and a direction of a major axis of the ellipse. 16. The system of claim 14, wherein a flat portion along the periphery of the pupil, if present, is recognized for identification purposes. 17. The system of claim 13, wherein a low frequency component of the two-dimensional Haar transform coefficients is obtained for identification purposes from a selected one of the input image signals. 18. The system of claim 13, wherein the Haar transform is selected from a set of Signum Haar functions. 19. The system according to claim 13, wherein the Haar transform is processed in a butterfly structure of the transform flow graph. 20. The system according to claim 13, wherein at least a selected one of the input image signals is analyzed in a variable multi-sector system of spectral transform using the Haar transform function. 21. The system of claim 20, wherein the multi-sector system of spectral analysis allows for variable selection of iris sectors that represent the visible portion of the iris for analysis purposes that are not distorted by interference. 22. The system according to claim 21, wherein the multi-sector system of spectral analysis allows defects or interference due to boundaries to be identified and excluded from the identification analysis. 23. A selected one of the input iris images is, for the purpose of said spectral analysis, an outer ring having an outer boundary located approximately at the sclera-iris boundary and inwardly spaced from the outer ring. 23. The system of claim 22, wherein the system divides into an inner annular portion having a radius spaced inwardly from the outer boundary by a predetermined distance. 24. The predetermined distance is between about 30-8.
24. The system of claim 23, which is zero pixels. 25. The outer ring, and the region in the annular zone disposed between the boundary of the annular zone with the outer ring and the periphery of the pupil, are respectively assigned to a plurality of sectors (n) and (p). Divide, where n is the same as or different from p, generate a sum of n + p sectors, and a predetermined value (m ² ) of the Haar coefficient
26. The system of claim 24, wherein is calculated using a Haar transform forming an mxm matrix. 26. n = 16, p = 16, m = 32 and 1024H for each of the 32 sectors.
26. The system of claim 25, wherein the aar function coefficients are calculated from a 32x32 matrix using a Haar transform. 27. The system of claim 26, wherein sectors obstructed or interfered by eyelids, eyelashes, etc. are rejected by adjacent sector Haar coefficient comparison. 28. The system of claim 25, wherein if the comparison of adjacent sector Haar coefficients detects a predetermined sharp variation in the high frequency coefficient, the sector is rejected as a defect during analysis. 29. After removing rejected sectors from the analysis, a subset of the entire set of m ² Haar coefficients is selected to pre-select from the m × m matrix of m ² Haar coefficients selected for analysis purposes. 29. The system of claim 28, wherein the system generates a defined number of coefficients. 30. When m = 32, the subset is Ha
30. The system of claim 29, comprising 31 coefficients selected from a 32x32 matrix of ar coefficients. 31. The system of claim 29, wherein the iris reference record is generated using a selection of a predetermined number of coefficients. 32. The system of claim 31, wherein said predetermined number of coefficients represents a selected low frequency zone used for identification purposes. 33. The control unit represents a pupil response by using the input image signal to generate a pupil histogram or pupillogram representing an average contraction and dilation of the pupil per time in response to a light stimulus. The system of claim 1, wherein the information is calculated. 34. The system of claim 1, wherein the camera has a variable capture or flex zone for autofocus. 35. The camera as set forth in claim 25, wherein:
The system of claim 1, further comprising a second lens having a focal length greater than mm. 36. The system of claim 33, wherein said pupilogram ensures image scanning of live animals. 37. The system of claim 1, wherein the iris image pickup unit further comprises a field-of-view guidance unit for aligning a user's eye with a camera lens. 38. The system of claim 35, wherein the plurality of infrared light sources are arranged annularly around a periphery of the second camera lens. 39. A system for testing for the presence of a drug or alcohol in a human, comprising: (a) a control unit for receiving identification information identifying a surviving animal; (b) an iris image An iris image pickup unit including a camera operated by a control unit for capturing an input image signal to generate an input image signal; and (c) a data processing unit for preprocessing the input image signal into processed data. A unit calculates the processed data resulting from processing of the input image signal to represent the average contraction and dilation of the iris pupil in response to light stimuli per time, whereby humans may be exposed to alcohol or drugs. Comparison between a person who is addicted to alcohol and a person who does not contain alcohol or drugs. , In order to have different shape of the curve of pupillogram, the system processes the data may be manipulated to determine whether an indicator of the current or past addiction. 40. A method for testing for the presence of at least one drug or alcohol in a human body, comprising: (a) obtaining a plurality of images of substantially the same portion of a human iris and pupil at different times. (B) processing the plurality of images into a pupil histogram; (c) calculating characteristics of the pupil histogram processed in step (b); and (d) based on the comparing step (c), Ascertaining the presence of at least one of a drug or an alcohol; 41. The method of claim 40, wherein the flatness characteristic of the curve associated with the pupil histogram indicates the presence of at least one of a drug and an alcohol. 42. A method for confirming the identity of a surviving animal, comprising: (a) first determining whether the surviving animal has an identity corresponding to the surviving animal identified in the database; (B) If the result of step (a) is yes,
Obtaining a plurality of images representing at least a portion of the iris and pupil of the living organism's eye; (c) processing the plurality of images to obtain a density and texture morphology of the iris fiber structure using a frequency conversion method, a pupil response, Obtaining iris identification parameter information selected from at least one of a pupil shape, an autonomic nervous ring reaction, an autonomic nervous ring shape, the presence of a depression, a position of the depression, and a shape of the depression; (d) Comparing the predetermined characteristics of the iris identification information obtained in step (c) with the iris identification information obtained in step (c);
And (e) verifying whether there is any indication of a match of the data compared in step (d); 43. The method of claim 42, further comprising the step of obtaining a plurality of images of each eye. 44. The input password information,
43. The method of claim 42, further comprising the step of starting step (b) of claim 42 by matching with pre-stored human identifiable information. 45. The method of claim 42, further comprising the step of illuminating the eye with a light source and capturing a plurality of said iris images in succession. 46. The method according to claim 45, wherein an infrared light source is used for the irradiation. 47. Aligning the camera with the user's eye,
43. The method of claim 42, further comprising the step of obtaining a plurality of images.
The method described in. 48. The method of claim 42, wherein said plurality of images are processed to obtain information relating to at least two of said identification parameters. 49. The method of claim 42, wherein each of said parameters is processed for identification purposes. 50. In the processing step (c), Wave
43. The method of claim 42, wherein a let transform is used. 51. Haar in the processing step (c)
The method of claim 50, wherein a transform is used. 52. The method of claim 42, wherein the processing step comprises analyzing an iris pupil shape using at least one of a curve fitting and edge detection algorithm. 53. The method of claim 52, wherein the processing step includes detecting a pupil ellipticity and a direction of a major axis of the ellipse. 54. The method of claim 53, wherein the processing step comprises measuring the presence of a flat portion along the periphery of the pupil. 55. The method of claim 51, wherein the processing step comprises obtaining low-frequency components of two-dimensional Haar transform coefficients from a selected one of the input image signals. 56. The method of claim 51, wherein the processing step comprises selecting a Haar transform from a set of Signum Haar functions. 57. The processing step uses a variable multi-sector system of spectral analysis to allow a variable selection of sectors of the iris representing visible portions of the iris to occur for analysis purposes that are not distorted by interference. 52. The method of claim 51, comprising: 58. The method of claim 57, further comprising using a variable multi-sector system of spectral analysis to eliminate defects or interference due to identified boundaries from the identification analysis. 59. The processing step includes dividing the iris image into a predetermined number of sectors located within the outer ring and an outer ring including the pupil and a concentrically formed annular region. Item 58. The method according to Item 57. 60. The method according to claim 59, wherein the sector is analyzed for a sector and an adjacent sector, and if a predetermined sharp variation in high frequency coefficient is detected, the sector is rejected as a defect. Method. 61. A processing step comprising: after removal of rejected sectors, measuring a subset of a predetermined number of all coefficients selected for analysis purposes and determining a predetermined subset of subsets for each selected sector. 61. The method of claim 60, wherein the selected number of coefficients is used to generate an iris reference record for use in comparing step (d) of claim 42. 62. The method of claim 61, wherein a coefficient representing a selected frequency zone of a selected sector is used for identification purposes. 63. The method of claim 42, wherein a plurality of iris images are obtained using autofocus of a camera having variable capture or flex zones. 64. An eye, using information stored in advance,
43. The method of claim 42, further comprising the step of automatically aligning the plurality of image sources in step (b) with a source. 65. A method for identifying a user's iris based on the response of the autonomic nervous ring associated with the pupil and iris, comprising: (a) retrieving pre-stored personal information from a database suitably accessed by the user; (B) acquiring a series of images of the user's iris by a camera; and (c) converting the image signal of the iris zone from a selected one of the acquired images of the iris. Initiating the iris fiber structure confirmation process by selecting and measuring the identity confirmation parameter from the image signal of the selected zone,
(D) detecting a zone of a pupil and an autonomic nerve ring from a selected one of the acquired iris images; and selecting a selected image signal of the detected zone of the pupil and the autonomic nerve ring. By calculating the dynamic response (contraction and dilation) of the pupil and the autonomic nervous ring from and measuring the relevant identification parameters,
Initiate the pupil and autonomic nervous system reaction confirmation process to confirm the identity of the user; (e) Confirm the identity of the user by comparing the identification parameters with the identification parameters previously stored as personal information; The above method, comprising the steps of: 66. The image acquisition process automatically adjusts the camera position according to the user's pre-stored personal information obtained from step (c) of claim 65; aligns and automatically focuses the camera. 66. The method of claim 65, comprising adjusting; turning on the light source to induce movement of the pupil and autonomic ring; extinguishing the light source. 67. An iris fiber structure verification process comprising: determining an identification parameter derived from a Wavelet transform coefficient associated with a corresponding iris fiber structure by converting a selected image signal of the iris by a Haar transform; 66. The method of claim 65, comprising verifying the identity of the user by retrieving corresponding pre-stored parameters from the user. 68. A method for identifying an iris based on a reaction of a pupil and an autonomic nervous ring in the iris, comprising: (a) retrieving pre-stored personal information from a database suitably accessed by a user; (B) acquiring a series of images of the iris by a camera; (c) detecting zones of a pupil and an autonomic nerve ring from a selected one of the acquired iris images; By calculating the dynamic response (contraction and dilation) of the pupil and the autonomic nervous ring from the selected image signal of the detected zone of the autonomic nervous ring and measuring the relevant identification parameters,
Initiate the pupil and autonomic nervous ring reaction confirmation process to confirm the identity of the user; (d) Confirm the identity of the user by comparing the identification parameters with the identification parameters previously stored as personal information; A method comprising the steps of: 69. The step (d) of claim 65, wherein the two-dimensional image signal from the detected autonomic nerve ring zone is converted by a central axis transformation, and the shape of the autonomic nerve ring in the detected zone is calculated. 66. The method of claim 65, comprising calculating a parameter for identifying a shape of the autonomic nerve ring. 70. A method for identifying an iris, comprising: (a) verifying the identity of a user by retrieving previously stored personal information from a database suitably accessed by the user; (b) Acquiring a series of images of the user's iris with a camera; (c) detecting the indentation zone from the acquired image, if any, and measuring identification parameters based on the shape and position of the indentation. By
Initiating a first indentation verification process to verify the identity of the user; 71. When no depression is present, the user's identity as to whether a depression is also present in the reference iris is confirmed, and when a depression is present in the reference iris, the user is identified. 71. The method of claim 70, further comprising a second dent confirmation process rejecting as inappropriate. 72. Detecting the pupil and autonomic nervous ring zones by calculating the central part of the acquired iris image; calculating the response (contraction or dilation) of the moving pupil and autonomic nervous ring in the detected zone. 72. The method of claim 71, further comprising: determining an identification parameter by determining the identity of the user by retrieving the corresponding parameter from a database; 73. Detect a zone of an autonomic nerve ring outside a zone associated with a selected iris for analysis; transforming a two-dimensional image signal at the detected autonomic nerve ring zone by a central axis transformation; Calculating parameters for identifying the shape of the autonomic nervous ring by calculating the shape of the autonomic nervous ring in the detected zone; confirming the identity of the user by searching the corresponding parameters from the database; 72. The method of claim 71, comprising:
The method described in. 74. A first dip identification process comprising: determining whether an dip is present by detecting an edge of the iris image from a selected acquired image signal of the iris; the dip as a result of the determining step Detecting the indentation zone if exists; converting the two-dimensional image signal in the detected indentation zone by central axis transformation, and calculating the identification parameter based on the shape and position of the indentation; 72. The method of claim 71, comprising: verifying a user's identity by searching a corresponding parameter. 75. A system for confirming the identity of a living animal by iris scanning, comprising: (a) a control unit for receiving identification information identifying a living animal, and access by the control unit. And
A data storage unit including a database for having predetermined personal information relating to an identifiable organism for comparison with the identification information; (b) when the identification information corresponds to the predetermined information, A binocular camera iris image pickup unit operated by a control unit for capturing an iris image of the right eye to generate an input image signal; and (c) a data processing unit for pre-processing the input image signal into processed data. A system comprising: