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WO2000046739A1 - Procede d'analyse d'empreintes de doigts - Google Patents

Procede d'analyse d'empreintes de doigts Download PDF

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WO2000046739A1
WO2000046739A1 PCT/US2000/003338 US0003338W WO0046739A1 WO 2000046739 A1 WO2000046739 A1 WO 2000046739A1 US 0003338 W US0003338 W US 0003338W WO 0046739 A1 WO0046739 A1 WO 0046739A1
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individual
finger
fingeφrint
ridges
ridge
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Amy S. Zelson
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/117—Identification of persons
    • A61B5/1171—Identification of persons based on the shapes or appearances of their bodies or parts thereof
    • A61B5/1172—Identification of persons based on the shapes or appearances of their bodies or parts thereof using fingerprinting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification

Definitions

  • the invention has applications in the fields of fingerprint measurement and analysis, forensics, and forensic anthropology.
  • Friction ridge skin is very different from the skin covering the rest of the body. On humans, this special skin is located on the fingertips, palms, soles and toes. In humans, dermatoglyphics (specific characters of ridged skin), begin to form around the 3 rd - 4 th month of fetal life and are usually fully developed around the 6 th month in utero. The actual markings of dermatoglyphics, which are visible to the naked eye, are described as "continuous and discontinuous alternating ridges and sulci" (Montagna and Parakkal 1974:10), similar to the markings or the stripes on a zebra.
  • Fingerprint Analysis for Identification and Authentication Dermatoglyphics have long been used as a tool in the identification of unknown individuals. Many attributes of dermatoglyphics make them useful in this regard. Four important factors about fingerprints make them valuable for personal identification (Holt 1968). Most importantly, fingerprints are not affected by age. They are also not affected by environment, except during in utero development. Their high variability and the fact that their patterns are so easily classified are also important characteristics of dermatoglyphics that contribute to their use for personal identification and other biological studies (Holt 1968:6). Fingerprints are cheap and easy to obtain, which make the data easily available and quite abundant. This enables a researcher to gather a large sample fairly quickly and practically, and makes possible routine use by law enforcement agencies.
  • An individual's fingerprints are typically recorded by taking an impression on one of various materials.
  • the method used most commonly by law enforcement and scientists is the ink and paper method. This method produces prints that are legible, with good detail, and ready for comparison.
  • the finger is coated with a thin layer of black ink and then pressed and rolled (in order to obtain a complete print) from one side to the other, on a piece of glossy paper. Due to the fact that ridges are elevated, a print is left behind which records the details of the pattern on that particular finger.
  • a finger may be coated with a chemical and then printed on a piece of paper.
  • the paper has been treated previously with another chemical that makes the print appear.
  • the paper is sprayed with a chemical that develops the print (Cowger 1983).
  • Another method for recording finger prints is to dust the fingers with a powder that is usually intended for use with latent prints, and then to lift the print from the finger with an adhesive material which is then placed against an opaque background (Cowger 1983). This however, produces a backward or reversed image of the print unless it is viewed through a transparent backing.
  • Some law enforcement agencies have moved toward digital scanning of finger prints which downloads the print directly into a computer data base.
  • latent prints or impressions of fingerprints accidentally left behind when a finger has normal contact with most surfaces, are found at a crime scene.
  • There are three different types of prints which are found at crime scenes (De Forest, Gaensslen and Lee 1983:341). Visible prints are left by a finger stained with a colored material such as blood, dirt or grease. Plastic prints, or impressions made by pressing on a soft surface such as wax or clay, can also be found at a crime scene.
  • latent prints which are not easily visible, are often left behind on smooth surfaces. These prints must be visualized with various techniques such as dusting with a powder, argon laser illumination, and the use of silver nitrate (De Forest, Gaensslen and Lee 1983:332).
  • Fingerprint matching is a reliable and widely used technique for personal identification or verification.
  • an increasingly common approach to identification involves scanning a sample fingerprint or an image thereof, converting it into electrical signals, and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint are then compared to information for reference fingerprints already in storage to determine or verify the identity of the individual providing the print. Devices employing such methods are being increasingly deployed as an element of security and access control systems.
  • Fingerprints are typically classified into a plurality of discrete sets and/or subsets in the form of a hierarchical tree to expedite searching.
  • Sir Edward Henry formulated a system of classification which enables prints to be quickly classified and easily located in a file (De Forest, Gaensslen and Lee 1983). This is the most widely used method in the United States for classifying finger prints (Cowger 1983). This method relies mostly on the finger print patterns themselves. Their corrugated markings had been previously classified by Sir Francis Galton in the late 1800s, into 3 major pattern types; loop, arch, and whorls (Galtonl965 [1895]). These three basic patterns have been further divided into eight subpatterns (De Forest, Gaensslen and Lee 1983:332).
  • a common top level classification for fingerprints usually differentiates the prints into the classes of: plain whorl, plain loop, tented arch, etc. based upon broad ridge pattern types. These classes are further divided into subclasses. A fingerprint sample to be searched, once classified, can be more efficiently compared to only those prints in the respective classes and subclasses of the search tree.
  • U.S. Pat. No. 5,465,303 to Levison et al. which describes both the widely used Henry classification system and the Vucetich classification system.
  • U.S. Pat. No. 5,140,642 to Hsu et al. (inco ⁇ orated herein by reference in its entirety) is directed to a method for determining the actual position of a core point of a finge ⁇ rint based upon finding ridge flows and assigning a direction code, correcting the ridge flows, and allocating the core point based upon the corrected direction codes.
  • U.S. Pat. No. 5,040,224 to Hara discloses an approach to preprocessing finge ⁇ rints to correctly determine a position of the core of each finge ⁇ rint image for later matching by minutiae patterns.
  • U.S. Pat. No. 3,959,884 to Jordan et al. discloses a method of classifying finge ⁇ rints by converting a finge ⁇ rint to a pattern of binary values which define a spatial array of ridges and valleys, and constructing a descriptor code from the binary values.
  • U.S. Pat. No. 4,151,512 to Riganati et al. (inco ⁇ orated herein by reference in its entirety) describes a finge ⁇ rint classification method using extracted ridge contour data.
  • the ridge flow in the finge ⁇ rint pattern and minutiae data are identified and extracted from a finge ⁇ rint pattern.
  • Topological data are extracted from the ridge contour data, and the extracted information is used to automatically perform classification of the finge ⁇ rint patterns and/or matching of the finge ⁇ rint pattern.
  • U.S. Pat. No. 4,185,270 to Fischer et al. discloses a process for encoding and verification based upon minutiae.
  • U.S. Pat. No. 4,210,899 to Swonger et al. discloses an optical scanning finge ⁇ rint reader cooperating with a central processing station for a secure access application such as admitting a person to a location or provide access to a computer terminal.
  • U.S. Pat. No. 4,525,859 to Bowles is also directed to minutiae matching and describes an automated system.
  • 4,947,443 to Costello discloses a method for verifying the identity of a person using four of six characteristics.
  • U.S. Pat. No. 4,747,147 to Sparrow discloses a finge ⁇ rint scanning system and method for rotating a scan line about a central point on the finge ⁇ rint. A code representing the types of irregularities is recorded, along with a ridge count so that coordinates give a complete topological and spatial description of a finge ⁇ rint for computer processing.
  • U.S. Pat. No. 5,363,453 to Gagne et al. (inco ⁇ orated herein by reference in its entirety) relates to a system and method for generating a numeric finge ⁇ rint identifier for inclusion on a magnetic strip.
  • U.S. Pat. No. 5,239,590 to Yamamoto discloses a finge ⁇ rint image processing method wherein a master and a sample finge ⁇ rint image are divided into a plurality of pixels with an associated direction. The direction of each pixel is determined based on pixel density partial differentials between the pixel and adjacent pixels for a plurality of directions.
  • U.S. Patent 5,974,163 to Kamei (inco ⁇ orated herein by reference in its entirety) describes a finge ⁇ rint image classification system employing "classification units" each of which generates a probability data set indicating the probability of a finge ⁇ rint image being classified into various categories; and a category decision unit for outputting a classification result according to the probability data set.
  • U.S. Patent 5,974,162 to Metz et al. (inco ⁇ orated herein by reference in its entirety) describes a device for forming and detecting finge ⁇ rint images, and a compact system for detecting the surface topography of the finger of an individual and producing images which can be electronically stored or transmitted.
  • U.S. Patent 6,005,963 to Bolle et al. describes a system and method for determining if a finge ⁇ rint image contains an image portion representing a partial finge ⁇ rint impression, involving dividing a finge ⁇ rint image into blocks of pixels.
  • mice and Kobyliansky (1986) used over 66 different variables including pattern indices, palmar ridge counts, pattern size and symmetry to develop a discriminate function for predicting sex. They were able to correctly classify 71.6 percent of their subjects by sex. However, a method employing 66 variables is too cumbersome for routine use.
  • Demarchi, Giordano and Marcellino embarked on a study using dermatoglyphics to examine inte ⁇ opulation relationships. Many features were analyzed, including finger ridge count, palmar ridge count, and pattern intensity indices for finger and palms. Males and females were included to examine the sexual differences as well. They determined that there were significant statistical differences between the sexes for the frequency of most of the variables, but did not develop a technique for predicting sex from dermatoglyphic traits.
  • finger tip ridge breadth is indeed correlated to sex.
  • the ability to predict an individuals sex, based on a finger tip ridge breadth measurement can be accurately done with the use of a specific formula created for that pu ⁇ ose.
  • the finge ⁇ rint does not need to be complete, as long as multiple unobstructed parallel ridges, preferably about ten such ridges, are present.
  • the specific finger the print comes from does not need to be known, this method can be applied to prints from unknown digits.
  • FIG. 1 shows a typical finge ⁇ rint, with the straight line representing an example of a measurement across ten parallel ridge lines with no obstructions.
  • DETAILED DESCRIPTION OF THE INVENTION The invention utilizes the average measurements from a sample of 500 individuals to create and test a discriminant function for predicting sex based on finger tip ridge widths. Average measurements from a larger sample would obviously provide an even more statistically robust model.
  • the invention is concerned with measurements of the fingertips only. As noted above, it is obvious that the definitions of the same techniques vary among the different practitioners. Although different systems of measurement have been used to study ridge breadth in the past, the present invention employs the technique of measuring across multiple parallel ridges, preferably between two and ten ridges. No previous technique has measured multiple parallel ridges regardless of the pattern type. In this study, the pattern type was considered independent of measurement and therefore would not affect the measurement. Every finger was measured regardless of pattern. Previous methods did not measure every finger because certain patterns lack a triradii or a core, which is part of the measurement when pattern is considered (Galton 1965; Bonnevie 1924; Mi, Budy and Rashad 1982). The particular embodiment of the invention described here focuses on ridge breadth and its relationship to sex, with pattern and other factors being ignored.
  • the method is advantageous because of its intended use in the field of forensic science. Many times there is only one finge ⁇ rint left at a crime scene. If that print happens to be an arch pattern, many previous methods (Hall and Kimura 1994; Galton 1965[1892]; Holt 1968) would not have permitted a measure of ridge breadth because the pattern does not contain a triradii. Regardless of the pattern type, the method proposed here can be employed on any finge ⁇ rint as long as it is readable, even on partial prints where the pattern may not be discernable, as long as there are multiple, and preferably two to ten, parallel ridges present. All of the measurements in the examples were made using exactly ten parallel ridges.
  • the object of the invention is to determine, from a finger tip's ridge breadth, the gender of an individual, many of the tests run were done separately for each sex. Therefore, both cumulative data and data for each of the sexes are presented. The sexes were equally represented, with 250 of each present. While the race of each individual was recorded, the extent of the disparity in sample size between the groups was such (81.6 percent were white) that it was decided to not include the race variable in this analysis.
  • the overall mean summed measurement of all ten fingers for the male sample alone is 5.58 cm with a standard deviation of .378 cm.
  • the overall mean summed measurement for all of the fingers combined in the female sample was 4.44 cm with a standard deviation of 0.31 cm.
  • weight and sex made significant contributions on their own, even when all of the other variables were included.
  • height although previously a bivariate correlation with ridge breadth, was not a useful predictor of ridge breadth, when the other two variables, sex and weight, were already in the model. Its individual contribution with the other variables was not significant.
  • the final procedure performed was a discriminant analysis.
  • a discriminant analysis was employed to develop a function that will enable one to predict group membership.
  • Formulas were developed for each of the ten fingers, however, for this specific study, the individual's average for all ten fingers was used since all of the fingers were highly interco ⁇ elated with each other and it is usually not known which finger leaves the print behind. It has already been noted by Holt (1959) that all of the fingers are correlated with one another. The highest correlations found were between adjacent fingers and homologous fingers (Holt 1951; Holt 1959). According to the discriminant analysis, 94.4 percent of the randomly selected group of males and females were correctly classified according to sex, while 94.8 percent of the validation group was correctly classified.
  • L2 was 76%
  • L3 was 100%
  • L4 was 90%
  • L5 was 90%.
  • the overall lowest score predicted for the male sample was at 57% on L5, while the lowest predicted score for the females was
  • the co ⁇ ect classification of sex from an unknown digital print could be between 57% and 100% of the time with an average correct classification of 82%. This result is significantly better than chance which could only predict accurately the sex of an individual 50% of the time.
  • the invention provides a method of determining the probable sex of an unknown individual, said method comprising the step of measuring the ridge width in one or more of said individual's finge ⁇ rints.
  • the ridge width is measured by measuring the overall width of a plurality of parallel ridges. In another emodiment, the ridge width is measured by measuring the overall width of from two to ten parallel ridges.
  • the method further comprises the step of comparing the measured ridge width to a data compilation which correlates ridge width with probable gender.
  • the step of comparing the measured ridge width to the data compilation is carried out by means of a computer, which outputs the probable gender.
  • the measurement of ridge width is carried out by a process of computerized image analysis.
  • x is the sum distance in centimeters, either measured or extrapolated, across 100 finge ⁇ rint ridges, (ii) A f is 34 ( ⁇ 5%), and (iii) B f is 75 ( ⁇ 5%) (the female formula); and if P m > P f , determining that the individual is probably male, and if P f > P m , determining that the individual is probably female.
  • the invention also encompasses a device comprising an image analyzer component which displays an image of the finge ⁇ rint of an individual, further comprising a means for inputting to the analyzer the coordinates of a line segment normal to a plurality of parallel finge ⁇ rint ridges, wherein said device computes the length of the line segment and computes the probable gender of the individual.
  • a data compilation which co ⁇ elates finge ⁇ rint ridge width with probable gender, which may be visually readable or in computer-readable format.
  • the invention encompasses a computer-readable medium, carrying the data compilation. In sum, the invention provides a method of predicting the probable gender of an individual based on a measurement of the individual's finger tip ridge breadth.
  • the finge ⁇ rints used in this study were obtained from the Butte County Sheriffs Department records of individuals a ⁇ ested in Butte County, California during 1998.
  • the sample itself is comprised of finge ⁇ rint sets from 500 individuals: 250 males and 250 females.
  • Each set of prints includes a set of rolled finge ⁇ rints for each of the ten individual fingers as well as a set of the flat right and left fingers taken simultaneously, with the thumbs printed separately.
  • Holt "It is important to have flat, as well as rolled impressions, of the fingers. They provide a check when the identity of any rolled print is in doubt" (1968:32).
  • each data sheet of finge ⁇ rints consisted of two prints for each of the ten digits. Additional information recorded on the same data form consisted of sex, race, height, and weight. All of the individuals used in this study were adults, aged 18 or older.
  • finge ⁇ rints do not change their pattern or structure throughout an individual's life, they do grow in size as an individual grows (Loesch 1983: 139). Thus, the limitation of the sample to adults eliminates potential spurious data stemming from juvenile fmge ⁇ rints.
  • An OLYMPUS SZ4045TR TM Zoom Stereo Microscope and a UNISLIDE TM mechanical positioner were used to view the finge ⁇ rints.
  • An ACU-RITE III TM digital readout system (Bausch & Lomb, Rochester N.Y.) was used to measure the ridges in millimeters. The increment being measured consisted often parallel ridges within the finge ⁇ rint.
  • the data sheet was placed under the microscope, then the ten ridges to be measured were aligned pe ⁇ endicular to the X-axis in the eye piece. Viewing the print under high power allowed the analyst to find ten parallel ridges with no obstructions or intervening digital minutia.
  • the actual spot chosen for counting was random, being the first spot noted to have ten parallel ridges with no bifurcation or other minutia which would cause a break in the line pe ⁇ endicular to the ridges chosen for measure. Bifurcations, where ridges divide into two, and anomalies inconsistent with ten parallel ridges were considered obstructions and were avoided.
  • the measurement began in a 'valley', halfway between two ridges, as shown in Figure 1. After counting over ten ridges, the measurement ended again, mid- valley. The increment machine was scrolled pe ⁇ endicular to the angle of the ridge lines, measuring the ridges in millimeters to a precision of 0.0001 mm. All of the measurements were taken from the first set of rolled finge ⁇ rints. If a finge ⁇ rint was smeared, too light, not fully printed or in any way unreadable, the measurement was taken from the second set of 'plain impressions', or flats, on the same record form.
  • SPSS Social Sciences
  • the overall mean measurement of all ten fingers for the male sample alone is 5.58 cm with a standard deviation of .378 cm.
  • the mean for the right hand was 5.61 cm with a standard deviation of 0.41 cm, while the mean for the left hand was marginally smaller at 5.54 cm with a standard deviation of .396 cm, as illustrated in Table 2.
  • the right thumb was once again the finger with the largest mean measurement at 5.94 cm with a standard deviation of 0.65 cm.
  • the finger with the smallest measurement among the male sample was the left pinky, with a mean of 5.31 cm and a standard deviation of 0.55 cm; this was a different result than obtained in the combined sex sample.
  • the single smallest measurement for the males was 3.74 cm and the single largest male measurement was 7.89 cm.
  • the overall mean measurement for all of the fingers combined in the female sample was 4.44 cm with a standard deviation of 0.31 cm.
  • the mean for the average of the right hand measurements was 4.51 cm with a standard deviation of 0.35 cm.
  • the mean for the average of the left hand fingers was 4.37 cm with a standard deviation of 0.34 cm, as illustrated in Table 3.
  • the finger with the smallest average mean among the females was the left ring finger, at 4.21 cm with a standard deviation of 0.48 cm.
  • the finger with the largest average measurement was also the right thumb with a mean of 4.77 cm and a standard deviation of 0.51 cm.
  • the smallest individual measurement of the female fingers measured was 2.93 and the largest female measurement recorded was 6.72 cm.
  • weight and sex made significant contributions on their own, even when all of the other variables were included.
  • height although previously a bivariate co ⁇ elation with ridge breadth, was not a useful predictor of ridge breadth, when the other two variables, sex and weight, were already in the model. Its individual contribution with the other variables was not significant. Therefore, the remaining analyses performed concentrated on the variables sex, weight and ridge breadth, as height had then been determined unnecessary.
  • the final procedure performed was a discriminant analysis.
  • a discriminant analysis was employed to develop a function that will enable one to predict group membership as well as determine which variables were most useful for that prediction.
  • a discriminant analysis is used to classify observations into two groups, in this case male and female, given that there is an existing sample that falls into known groups. In other words, with information from an existing sample of cases, a prediction is possible, with a certain amount of accuracy, to which group a new case will belong.
  • a cross validation procedure was employed.
  • Predicted group membership for a case is determined by the value of the Fisher's classification function, with a case being placed in the group that represents the classification function of the largest value.
  • a measurement from a finger print of an unknown individual can be inserted into the two formulae developed.
  • One formula predicts female classification and the other predicts male classification.
  • the classification function for the male group was 42.823 ⁇ :-120.124, while the classification function for the female group was 33.868J -75.395, as illustrated in Table 5.
  • the measurement for the width often parallel ridges was substituted for x in each function. Table 5.
  • the formula Ax - B generates a predictive number for each sex, where for the male formula A is about 43 and B is about 120, and for the female formula A is about 34 and b is about 75. If the male formula generates the larger value, the individual is predicted to be male, if the female formula generates the larger value, the individual is predicted to be female. It is expected that values within about 5% of the given values of A and B will be operational. More generally, then, one can employ a function 43x - 120 as the male function, and 34x - 75 for the female function, with equivalent results.
  • a data compilation may be assembled, wherein any given ridge width is associated with a probability that the individual is male or female.
  • the data compilation may be as simple as a printed table or graph, or may be stored in computer-readable form for use by a computer.
  • Table 6 illustrates that 94.4 percent of the randomly selected group of males and females were co ⁇ ectly classified. Only 14 individuals, of the total number of 250, were misclassified. 94.8 percent of the validation group was correctly classified, leaving only
  • the initial Fisher's function presented here was developed using the average of all of the ten fingers from each of the 500 individuals. Twelve more functions were created, one using the averages of the right hand only, the left hand only and then one function for each specific finger. Although it is usually not known which finger has left a print behind, because many are partials, sometimes it can be discerned. If there are opposing prints, there is a good chance that a thumb and an index finger are present. In these cases, the individual fmger functions or the specific hand function, if more than three prints are present, can be employed.
  • the classification function coefficients for the hand averages and individual fingers are presented in Table 7, and the respected classification results are presented in Table 8.
  • the percent of the validation group correctly classified using the right hand average was 93.2 and the left hand was 94.0 percent. These results are very close to the predicted percent using the average from all ten fingers (94.8%). However, the functions employed for the individual fingers were not as successful as the functions using the averages.
  • the percent of the validation group correctly classified using the Rl function was 85.6%, LI was 87.2%, R2 was 83.2%, L2 was 80.4%, R3 was 89.2%, L3 was 88.0%, R4 was 82.3%, L4 was 88.0%, R5 was 86.7%, and L5 was 84.3%.
  • the individual fingers may not have as accurate results as the overall or hand averages because, when using averages, the abe ⁇ ant and unusual scores are balanced out from the larger sample size; with individual fingers, deviant numbers have more of an influence on the results. Table 7.
  • the data suggest using the formula created for the ten fmger average measurement if the specific finger is unknown. Of course, if the finger or fingers can be determined, then that specific formula or the formula for that hand average would be more appropriate. However, when a partial or isolated print from an unknown finger is found, the formula chosen for determining the sex of that individual should be the ten fmger average formula.
  • this formula was created using the average measurements of all of the fingers of both hands, generating a result that averages out deviant or aberrant measurements. This final test was run so that a print, from any finger, found at a crime scene, will have a predicted accuracy based on the ten fmger average formula.
  • the percentage correctly classified using the formula for the ten finger average for the females was Rl 83%, R2 was 77%, R3 was 87%, R4 was 93%, R5 was 93%, LI was 87%, L2 was 76%, L3 was 100%, L4 was 90% and L5 was 90%.
  • the overall lowest score predicted for the male sample was at 57% on L5, while the lowest predicted score for the females was 76% on L2. Both the males and females had a finger with a predicted score of 100%, LI and L3 respectively.
  • their overall accuracy for predicting sex, using the ten fmger average formula applied to each specific fmger and then combining the results was 87.3%.
  • the overall score for all of the fingers combined, predicting sex using the formula for the ten finger average with each individual finger measurement was 82.0%. What this illustrates is, if a random finger print from an unknown finger is found, using the formula created for the ten finger average, one could predict the sex of the individual accurately between 76% and 100% of the time for an average co ⁇ ect classification of 87.3% for females. For males, the correct classification of sex from an unknown digital print could be between 57% and 100% of the time with an average correct classification of 82%. This result is significantly better than chance which could only predict accurately the sex of an individual 50% of the time.
  • the statistics employed here illustrate the utility of fmger tip ridge breadth measurements in determining probable gender.
  • the T-Test determined that this difference was significant. Pearson's bivariate co ⁇ elation indicated that there was a strong relationship between sex, and ridge breadth.
  • the Partial F-Test tested the strength of this relationship, and sex had strong predictive value.
  • a discriminant analysis was performed to determine if an individual's sex or weight category could be predicted using a finger tip ridge breadth measurement.

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Abstract

L'invention concerne un procédé servant à déterminer le sexe probable d'un individu à partir de ses empreintes de doigts. Ledit procédé repose sur une étroite corrélation entre la largeur des crêtes du bout du doigt et le sexe, indépendamment de la taille corporelle. Ce procédé est utile dans les domaines de la justice, de la médecine légale, et de l'anthropologie légale.
PCT/US2000/003338 1999-02-08 2000-02-08 Procede d'analyse d'empreintes de doigts Ceased WO2000046739A1 (fr)

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AU29877/00A AU2987700A (en) 1999-02-08 2000-02-08 Fingerprint analysis method

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Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1227429A3 (fr) * 2001-01-29 2002-08-14 Nec Corporation Dispositif et méthode de reconnaissance d'empreintes digitales
FR2828571A1 (fr) * 2001-08-10 2003-02-14 Sagem Procede de reconnaissance d'empreintes digitales par zoom d'image
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EP1227429A3 (fr) * 2001-01-29 2002-08-14 Nec Corporation Dispositif et méthode de reconnaissance d'empreintes digitales
FR2828571A1 (fr) * 2001-08-10 2003-02-14 Sagem Procede de reconnaissance d'empreintes digitales par zoom d'image
US8184873B2 (en) 2003-04-04 2012-05-22 Lumidigm, Inc. White-light spectral biometric sensors
WO2005059805A2 (fr) 2003-12-11 2005-06-30 Lumidigm, Inc. Procedes et systemes destines a l'estimation de caracteristiques personnelles a partir de mesures biometriques
EP1700246A4 (fr) * 2003-12-11 2008-06-11 Lumidigm Inc Procedes et systemes destines a l'estimation de caracteristiques personnelles a partir de mesures biometriques
US8229185B2 (en) 2004-06-01 2012-07-24 Lumidigm, Inc. Hygienic biometric sensors
US8913800B2 (en) 2004-06-01 2014-12-16 Lumidigm, Inc. Optical biometrics imaging with films
US8165357B2 (en) 2004-06-01 2012-04-24 Lumidigm, Inc. Two camera biometric imaging
US8787630B2 (en) 2004-08-11 2014-07-22 Lumidigm, Inc. Multispectral barcode imaging
US8175346B2 (en) 2006-07-19 2012-05-08 Lumidigm, Inc. Whole-hand multispectral biometric imaging
US8781181B2 (en) 2006-07-19 2014-07-15 Lumidigm, Inc. Contactless multispectral biometric capture
US8831297B2 (en) 2006-07-19 2014-09-09 Lumidigm, Inc. Contactless multispectral biometric capture
US8285010B2 (en) 2007-03-21 2012-10-09 Lumidigm, Inc. Biometrics based on locally consistent features
US8355545B2 (en) 2007-04-10 2013-01-15 Lumidigm, Inc. Biometric detection using spatial, temporal, and/or spectral techniques
EP2360619A4 (fr) * 2008-12-19 2012-05-30 Miaxis Biometrics Co Ltd Procédé de recherche d'empreinte digitale rapide et système de recherche d'empreinte digitale rapide
US8731250B2 (en) 2009-08-26 2014-05-20 Lumidigm, Inc. Multiplexed biometric imaging
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US8570149B2 (en) 2010-03-16 2013-10-29 Lumidigm, Inc. Biometric imaging using an optical adaptive interface
WO2013027011A1 (fr) 2011-08-23 2013-02-28 Sheffield Hallam University Catégorisation de dépôts biologiques à l'aide d'une spectrométrie de masse par désorption-ionisation laser assistée par matrice
WO2014011127A3 (fr) * 2012-06-15 2014-03-20 Sagiroglu Seref Système intelligent pour estimer le sexe uniquement à partir d'empreintes
CN109726270A (zh) * 2018-12-11 2019-05-07 徐炜华 一种基于文章分割和皮尔森检验的文章重复程度检测方法
CN109726270B (zh) * 2018-12-11 2022-11-25 徐炜华 一种基于文章分割和皮尔森检验的文章重复程度检测方法

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