CN106503517B - A security authentication system based on brainprint collection of virtual reality helmet - Google Patents

Abstract

The invention discloses a security authentication system and method based on brain pattern collection. The system includes: a data access module, a brain pattern generation module and a security authentication module; the data access module generates and The first brainprint feature parameter corresponding to the user identity information; the brainprint generation module obtains the second brainprint feature parameter generated by the user under the stimulation of audio and video signals; the security authentication module combines the first brainprint feature parameter with the second brainprint feature The parameters are compared, and the user identity and security authentication are carried out according to the comparison results, which solves the problem that there is no security authentication system based on brainprint collection in the prior art, and the brainprint password is not easy to be stolen. The advantage of being easy to be copied and abused, in extreme cases, if the brainprint password is leaked, the user can easily reset the brainprint password.

Description

A security authentication system based on brainprint collection of virtual reality helmet

technical field

The invention relates to the field of security authentication, in particular to a security authentication system based on virtual reality helmet brainprint collection.

Background technique

Electroencephalogram (ElectroEncephaloGram, EEG) is a nonlinear random signal, with period, amplitude and phase as the basic factors of its waveform. Variety. Research by neuroscientists has confirmed that there are at least four different brain waves in the brain: "α" (Alpha) brain waves, whose frequency is 8-12Hz, "β" (Beta) brain waves, whose frequency is 14-100Hz, "θ" (theta) brain waves have a frequency of 4-8 Hz, and "δ" (delta) brain waves have a frequency of 0.5-4 Hz. Brain wave activity (activity) refers to the EEG wave or wave continuity (such as β activity, α activity, etc.); brain wave rhythm (rhythm) refers to the brain electrical activity (such as β Rhythm, alpha rhythm, spine rhythm, etc.); complex (complex) is defined as a characteristic waveform, or a relatively constant waveform that recurs repeatedly, and two or more waves that are different from background activity are continuous (Such as spike-slow complex, sharp-slow complex, K-complex, etc.).

Brain pattern is the characteristic of the measured brain wave signal. The brains of different people will have different reactions to a specific thing, thus producing different characteristics of brain waves. Through the external performance characteristics of brain waves, it can be used as A basis for identification of a particular thing. The response of brain waves to certain things is unique, and everyone is different. It is referred to as "brain pattern" by scientists. If this response is used as the only password for verification, the encryption effect is very difficult to crack.

The current security authentication system mainly adopts digital password authentication, picture password authentication or biometric authentication. Among them, biometric identification methods include: fingerprint recognition, face recognition, iris recognition, voice recognition, hand shape recognition, palmprint recognition, signature recognition, gait recognition, etc.

Among various biometric identification methods, fingerprint identification is the most widely used. Fingerprints are the uneven lines on the skin on the front of your fingers. Although a fingerprint is only a small part of human skin, it contains a lot of information. The lines of these finger skins vary in pattern, breakpoints and intersections, which we call "features" in information processing, and these features are different for each finger. Moreover, the fingerprint characteristics of a person are innate and basically formed after 6 months of birth. After that, the ridge type, structure, and overall distribution of statistical features of the fingerprint will no longer change significantly. Relying on the uniqueness of fingerprint features, we can associate a person with his fingerprints, and verify his true identity by comparing his fingerprint features with the pre-saved fingerprint features.

Compared with fingerprints, brainprints have many dynamic characteristics. Brainprints can be used as a method of identifying a terrorist or other dangerous individual by measuring his "brainprint" when he is shown a particular written document or image with which he was previously familiar, such as a training camp or a manual. Brainprint can also be used for identification, just like fingerprints and iris, it can be applied to many security authentication occasions.

When it comes to identifying individuals, brainprints offer some potential advantages over fingerprints. For example, if someone's fingerprints are stolen, there is little recourse they can take back because fingerprints are "irrevocable." There is the possibility of embezzlement and invalidation when fingerprints are used as security authentication. When the fingers are peeled due to dry weather and other reasons, it will cause abnormal recognition. When the fingerprint is stamped, it is easy to be abused. In the United States, which has always been known for its strict security measures, more than 6 million user fingerprints have been leaked. The brain pattern is different, it can be undone. In the rare event that a hacker does manage to steal a "brainprint" from an authorized user, the authorized user can also reset the "brainprint".

Brainprints can also be used to identify a person's "cognitive signature," quickly assessing a visitor's mental state. In order to judge whether the visitor's cognitive state and mental state are normal, and whether the visitor can enter the system. For example, if an air traffic controller is too tired, or distracted, he may not be able to enter the system even if his authentication is successful. If a pilot is in a good state of mind before entering the system, and there is an abnormality during the flight, after the brain pattern collection device in the pilot's helmet detects the abnormal brain pattern, it can send an alarm message to the ground station for remedial measures.

Although brainprint has many advantages in security authentication, there is currently no security authentication system and method based on brainprint collection.

Contents of the invention

In view of the above problems, the present invention provides a security authentication system and method based on brainprint collection.

The present invention provides a security authentication system based on brain pattern collection, including:

The data access module is used to receive the input of user identity information, and combine with the preset local database of brain patterns to generate the first brain pattern characteristic parameters corresponding to the user identity information. The local brain pattern database stores several user identities Information and brainprint feature parameters corresponding to the user identity information;

The brain pattern generation module is used to obtain the brain wave digital signal generated by the user under the stimulation of audio and video signals, perform feature extraction and analysis on the brain wave digital signal, and obtain the second brain pattern characteristic parameter;

The security authentication module is used to compare the first brainprint feature parameter with the second brainprint feature parameter, and identify and securely authenticate the user identity according to the comparison result.

The present invention also provides a security authentication method based on brain pattern collection, comprising the following steps:

Receive the input of user identity information, combine with the preset local database of brainprint to generate the first brainprint feature parameter corresponding to the user’s identity information, the local brainprint database stores several pieces of user identity information and The characteristic parameters of the brain pattern corresponding to the information;

Obtaining digital brainwave signals generated by the user under the stimulation of audio and video signals, performing feature extraction and analysis on the digital brainwave signals, and obtaining second brainprint feature parameters;

The first brainprint characteristic parameter is compared with the second brainprint characteristic parameter, and the user identity is identified and security authenticated according to the comparison result.

The beneficial effects of the present invention are as follows:

In the embodiment of the present invention, the data access module generates the first brainprint characteristic parameters corresponding to the user identity information according to the preset local database of brainprints; the brainprint generation module obtains the second brainprint generated by the user under the stimulation of audio and video Pattern characteristic parameters; the security authentication module compares the first brainprint characteristic parameters with the second brainprint characteristic parameters, and performs identification and security authentication on the user identity according to the comparison results, which solves the problem that there is currently no one based on The problem of the security authentication system for brainprint collection has the advantage that the brainprint password is not easy to be stolen, copied and abused. In extreme cases, if the brainprint password is leaked, the user can easily reset the brainprint password.

Description of drawings

Fig. 1 is a structural block diagram of a security authentication system based on brainprint collection according to a device embodiment of the present invention;

Fig. 2 is a schematic diagram of brain wave analog signals collected by the brain wave acquisition sub-module of the device embodiment of the present invention within a certain time interval

Fig. 3 is a structural block diagram of an example of a virtual reality helmet in the device embodiment of the present invention;

4 is a schematic diagram of the interaction between the brain pattern generation module and the batch processing module of the brain pattern in the security authentication system based on brain pattern collection in the device embodiment of the present invention;

Fig. 5 is a schematic diagram of a security authentication system based on brainprint collection according to an embodiment of the device of the present invention;

Fig. 6 is a flow chart of the security authentication method based on brainprint collection according to the method embodiment of the present invention.

Detailed ways

In order to solve the problem that there is no security authentication system and method based on brainprint collection in the prior art, the present invention provides a security authentication system and method based on brainprint collection. For further details. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

According to the device embodiment of the present invention, a security authentication system based on brainprint collection is provided. Fig. 1 is a schematic structural diagram of the security authentication system based on brainprint collection in the device embodiment of the present invention. As shown in Fig. 1, according to the present invention The security authentication system based on brainprint collection in the embodiment of the device of the invention includes: a data access module 110, a brainprint generation module 120, and a security authentication module 130, and each module of the device embodiment of the invention will be described in detail below.

Specifically, the data access module 110 is configured to receive the input of user identity information, combine with the preset local database of brain prints, and generate the first brain print characteristic parameters corresponding to the user identity information, the local brain print database stores Several pieces of user identity information and brainprint feature parameters corresponding to the user identity information.

In the device embodiment of the present invention, the data access module 110 can use various terminals, such as computers. The data access module 110 is also used to trigger the brain print generation module when receiving the input of the identity information of the new user in the new user mode.

The brainprint generation module 120 is used to acquire digital brainwave signals generated by the user under the stimulation of audio and video signals, perform feature extraction and analysis on the digital brainwave signals, and obtain second brainprint feature parameters.

The brain pattern generation module 120 is also used to obtain the brain wave digital signal generated by the new user under the stimulation of audio and video signals after receiving the trigger of the data access module 110 in the new user mode, and to the Feature extraction and analysis are performed on the digital brain wave signal to obtain new brain pattern feature parameters, which are associated with the new user identity information and stored in the brain pattern local database.

The brainprint generation module 120 is also used to associate the second brainprint with the user identity information and store it in the local brainprint database through the wireless transceiver module, so that the user can quickly view and process the currently recorded brainprint and analysis.

Current neuroscience research proves that the same person's brain responds almost identically to the same picture or the same piece of audio and video. The principle of selecting video and audio is to enable each person to produce a special and different response from others under the stimulation of the video and/or audio signal.

Specifically, the brain pattern generation module 120 includes a brain wave acquisition sub-module, a brain wave feature extraction sub-module, and a brain pattern analysis sub-module.

More specifically, the brain wave acquisition sub-module is used to acquire the brain wave analog signal generated by the user under the stimulation of the video signal, and amplify, filter, and analog-to-digital convert the brain wave analog signal to obtain the brain wave digital signal. Fig. 2 is a schematic diagram of brain wave analog signals collected by the brain wave acquisition sub-module of the device embodiment of the present invention within a certain time interval. In brain waves, it is common to see multi-morphic composite waveforms composed of β, α, θ, and δ waves overlapped disorderly. Quantity (index or index).

The brainwave acquisition sub-module can be a virtual reality helmet, virtual reality glasses or other head-mounted multimedia devices. Fig. 3 is a structural block diagram of an example of a virtual reality helmet in the device embodiment of the present invention. Specifically, the virtual reality helmet includes a helmet bracket, brain wave acquisition electrodes, video playback unit, audio playback unit, low noise amplifier, filter, high-precision analog-to-digital conversion chip, DC power supply, brain wave feature extraction sub-module, brain pattern generation Sub-modules, storage modules, wireless transceiver modules, etc. Among them, the brain wave acquisition electrodes, low noise amplifiers, filters, high-precision analog-to-digital conversion chips, brain wave feature extraction sub-modules, and brain pattern analysis sub-modules all belong to the brain pattern generation module.

Wherein, several electroencephalogram collecting electrodes are arranged on the helmet bracket, and are used for collecting the brain wave analog signal that the user produces under the stimulation of the content played by the video playback unit and/or audio playback unit; The low noise amplifier, filter, The analog-to-digital conversion chip is used for amplifying, filtering and analog-to-digital conversion of the brain wave analog signal to obtain the brain wave digital signal.

The internal bracket of the virtual reality helmet can arrange brain wave acquisition electrodes in multiple specific positions. The electrodes are placed outside the skull and are in close contact with the scalp through dry electrodes to collect scalp electrode EEG. Now the international 10-20 system is routinely used for scalp electrode EEG. The 10-20 system includes 19 recording electrodes and 2 reference electrodes. The arrangement of electrode positions is proportional to the size and shape of the skull, that is, no matter whether the head is large or small or the shape of the skull varies, the placement positions are comparable, because the 10-20 system is placed according to the percentage of the head itself. While the user is using the VR glasses in the virtual reality (VR) helmet, the electric wave acquisition electrode collects the corresponding brain wave analog signal of the user, and the brain wave analog signal passes through the amplifier (because the EEG signal is very weak, it is mv or uv level, and it is obtained After the attenuation of the skull and scalp, it needs to be magnified by millions of times to be displayed), filter (to reduce interference), high-precision analog-to-digital conversion, and finally form a brain wave digital signal.

In order to obtain more effective digital brain wave signals to facilitate the extraction of brain pattern characteristic parameters, the brain pattern generation module of the device embodiment of the present invention also includes a control sub-module for analyzing the brain wave generated by the user under the stimulation of audio and video signals within a period of time. Brain wave digital signal, if the feature of the brain wave digital signal is not obvious, the audio and video signal is changed.

The brain wave acquisition sub-module of the device embodiment of the present invention uses a virtual reality helmet to collect brain waves by wearing a virtual reality helmet to watch virtual reality videos or pictures, making the traditional boring brain wave acquisition process more entertaining. By adding an audio playback system, the user's experience is more pleasant, and it is beneficial to generate more brainprint features, and the helmet bracket in virtual reality can place multiple electrodes at specific positions to meet the requirements for brain wave collection.

The brainwave feature extraction submodule is used to perform feature extraction on the brainwave digital signal according to the brainprint feature parameters retrieved from the brainprint local database to obtain the brainwave digital signal containing the brainprint feature parameters .

In the device embodiment of the present invention, feature extraction includes but is not limited to the following methods: clustering of EEG signals, multiple linear regression, power spectral density calculation, mean and variance calculation, wavelet transform or Fourier transform followed by time domain Frequency domain analysis, channel characteristic analysis, Lyapunov exponent calculation, principal component analysis and independent component analysis, common spatial pattern method, sparse coding, integrated support vector machine and matched filter method.

The brain wave feature extraction sub-module is also used to intercept part of the brain wave digital signal containing the brain pattern feature parameters, compress and output. The purpose of intercepting part of the data is to simplify the data and reduce the data sent to the brainprint batch processing module. Methods for intercepting part of the data include but are not limited to the following methods: retain the brain wave simulation signals containing the EEG characteristic parameters and eliminate the brain wave simulation signals repeatedly containing the same EEG characteristic parameters. The input of the brainwave feature extraction sub-module is the brainwave digital signal and the brainprint feature parameters in the brainprint local database, and the output is the brainwave digital signal containing the brainprint feature parameters and the filtered brainwave digital signal.

Further, the security authentication system also includes a brainprint batch processing module, a brainprint feature and identity information cloud database module.

The brainprint batch processing module is used for clustering and processing the received batches of brainwave digital signals of the same user to obtain new brainprint characteristic parameters of the user.

Specifically, the brain pattern batch processing module can be deployed on a distributed big data cloud platform, and the software and hardware resources of the cloud platform are used to realize the feature extraction of brain waves. The big data cloud platform already has large sample statistical analysis, On the basis of deep learning neural network, machine learning and other clustering algorithms, according to the actual characteristics of the brain wave feature extraction project, the software and hardware resources of the cloud platform can be evaluated from several aspects such as algorithm operation efficiency, feature extraction accuracy, and feature validity. Performance is optimized. The brain pattern batch processing module includes a brain wave clustering sub-module, and a brain wave feature extraction and enhancement sub-module.

The brain wave clustering sub-module is used to cluster the received batches of brain wave digital signals of the same user to obtain a brain wave parameter clustering result.

The brainwave clustering sub-module clusters the characteristic parameters of the brainwaves of the current user and the characteristic parameters of the brainwaves of users who have used the brainprint collection virtual reality helmet and other brainprint collection virtual reality helmets to find out the brainwave characteristics of all users. The distribution and regularity among the characteristic parameters of the brain wave parameters. The input of the brain wave clustering module is the filtered and compressed brain wave digital signal of the brain wave feature extraction sub-module, and the output is the clustering result of the brain wave parameters of all users.

The brainwave feature extraction and enhancement sub-module is used to obtain new brainprint feature parameters based on the known brainwave features and implicit correlations between brainwave parameters and according to brainwave parameter clustering results.

The brain wave feature extraction and enhancement sub-module uses the association algorithm to mine association rules, utilizes the implicit association relationship between feature key information and the known features of brain waves, and retains the effective EEG feature parameters with high recognition rate. The electrical characteristic parameters are summarized as brainprint characteristic parameters. The input of the brain wave feature extraction and enhancement sub-module is the brain wave parameter clustering result and the known features of the brain wave, and the output is the brain pattern feature parameter.

There are many known characteristics of brain waves, for example: the frequency of α brain waves is 8-12 Hz, the frequency of β brain waves is 14-100 Hz, the frequency of θ brain waves is 4-8 Hz, and the frequency of δ brain waves is 0.5-4 Hz. Another example: when "cognizing" a certain target stimulus (singular stimulus), a group of waves can be recorded from the scalp, mainly including N1, P2, N2, and P300 (P3), which are collectively called event-related potentials (ERP). The latency of P300 potential elicited by the target stimulus is shorter than that of the non-target stimulus, and the amplitude is also higher. The difficulty of the task can affect the latency and amplitude changes. The greater the difficulty, the more obvious the P300 potential. In addition, whether there is a reward after completing the trial, that is, the incentive value of the stimulus also affects the P3 wave.

The brainprint feature and identity information cloud database module is used to receive the new brainprint feature parameters and feed them back to the local brainprint database, so that the local brainprint database can update the brainprint corresponding to the user.

The brainprint feature and identity information cloud database stores the brainprint, user identity information and brainprint feature parameters collected by all brainprint collection virtual reality helmets within a certain period of time. The brain pattern is obtained from the local brain pattern database to update the brain pattern database, increasing the data volume of the cloud platform's brain pattern characteristics and identity information cloud database, which is convenient for statistical analysis and network query. The brain wave feature extraction and enhancement sub-module outputs the brain pattern feature parameters to the cloud database. The brainprint feature and identity information cloud database outputs the brainprint feature parameters to the brainprint local database. The brainprint feature and identity information cloud database provides a brainprint database interface for software programs such as scientific research, medical treatment, sports, entertainment, and business applications. The input of the brainprint feature and identity information cloud database is the brainprint feature parameters of the brainwave feature extraction and enhancement sub-module and the brainprint data of the local database of the brainprint, and the output is the brainprint database interface and the brainprint parameter category.

The brain pattern analysis sub-module is used to analyze and process the brain wave digital signal containing the brain pattern characteristic parameters to obtain the brain pattern characteristic parameters, and the brain pattern characteristic parameters are the same as those retrieved from the brain pattern local database. After correlating the user identity information, the brainprint feature parameters corresponding to the user are obtained, and according to the preset normal brainprint feature parameters, it is judged whether the brainprint feature parameters corresponding to the user are normal or abnormal. The characteristic parameter of the brain pattern is the second characteristic parameter of the brain pattern, and the second characteristic parameter of the brain pattern is sent to the security authentication module; if it is abnormal, the characteristic parameter of the brain pattern corresponding to the user is sent to the preset abnormal In the brainprint information interface.

The input of the brain-print analysis sub-module is the brain wave digital signal including the brain-print feature parameters and the user identity information in the brain-print local database, and the output is the second brain-print feature parameters and abnormal brain-print feature parameters. The abnormal brain pattern feature parameters are used to judge whether the currently recorded brain pattern is valid, and can also judge whether the user has an abnormal mental state or cognitive state.

Fig. 4 is a schematic diagram of the interaction between the brainprint generation module, the batch processing module and other modules in the security authentication system based on brainprint collection in the device embodiment of the present invention. A certain number of brainprint feature parameters and user identity information generated by using the current brainprint collection virtual reality helmet are stored in the brainprint local database. Based on the brain pattern collection virtual reality helmet, the brain pattern feature parameters generated by the brain pattern generation module are transmitted to the brain pattern local database through the wireless transceiver module, which is convenient for users to quickly view, process and analyze the currently recorded brain pattern feature parameters. The brain pattern local database outputs the user identity information to the brain pattern analysis sub-module for the brain pattern characteristic parameters to be associated with the identity information. The brain pattern local database outputs the brain pattern feature parameters to the brain pattern feature extraction sub-module for the brain pattern feature extraction module to quickly process the brain wave information to obtain the brain pattern parameters. The input of the local database of the brain pattern is the characteristic parameters of the brain pattern generated by the brain pattern generation module and the identity information of the user when logging in, and the output is the interface of the local database of the brain pattern, the characteristic parameters of the brain pattern generated by the brain pattern generation module, and the User identity information and brainprint feature parameters.

The security authentication module 130 is configured to compare the first brainprint feature parameter with the second brainprint feature parameter, and perform identification and security authentication on the user identity according to the comparison result.

Specifically, the security authentication module 130 includes a comparison submodule, an identity identification submodule, and a security authentication submodule;

The comparison sub-module is used to compare the first brainprint feature parameter with the second brainprint feature parameter to obtain a comparison result;

The identity recognition sub-module is used to determine whether the user identity is an authorized user or an unauthorized user according to the comparison result, if the similarity between the user's first brainprint characteristic parameter and the second brainprint characteristic parameter is within an acceptable range, Then the user is an authorized user; if the similarity between the user's first brainprint feature parameter and the second brainprint feature parameter is within an unacceptable range, or the user's second brainprint feature parameter is consistent with all brainprint feature parameters in the local brainprint database If the feature parameters do not match, the user is an unauthorized user;

The security authentication sub-module is used to open permissions to authorized users, give early warning of visitor intrusion to unauthorized users, and store the second brainprint feature parameters into the preset non-real-name-authenticated brainprint sub-database.

Fig. 5 is a schematic diagram of a security authentication system based on brain pattern collection according to an embodiment of the device of the present invention. As shown in Fig. 5, the brain pattern is collected through the brain pattern collection virtual reality helmet or other brain pattern collection tools, and the brain pattern is combined with the user identity The claimed information is compared with the brainprint, and if the similarity of the brainprint to a certain brainprint in the brainprint database is within an acceptable range, the user is considered to be a pre-registered user in the brainprint database.

In the embodiment of the device of the present invention, the cloud platform and big data are used to process the brain pattern, so that the brain wave data can better serve scientific research and engineering, and the extracted brain pattern is more accurate. At the same time, after storing more brainprint data in the database of the cloud platform, it is beneficial to discover some unknown brainprint features and other brain wave phenomena through the statistical analysis method of large samples. Moreover, the brain pattern batch processing module accurately extracts and predicts the characteristics of brain waves through large sample statistical analysis, deep learning neural network and other methods, thus providing parameter basis for online brain pattern extraction. The offline system uses the software and hardware resources in the cloud platform for calculation.

According to the method embodiment of the present invention, a security authentication method based on brainprint collection is provided. FIG. 6 is a flow chart of the security authentication method based on brainprint collection in the method embodiment of the present invention. As shown in FIG. 6, according to the present invention The security authentication method based on brainprint collection in the embodiment of the invention includes the following processing:

Step 601, receiving the input of user identity information, combined with the preset local brainprint database, to generate the first brainprint characteristic parameters corresponding to the user identity information, the brainprint local database stores several user identity information and the corresponding The brainprint feature parameters corresponding to the user identity information;

Step 602, acquiring digital brainwave signals generated by the user under the stimulation of audio and video signals, performing feature extraction and analysis on the digital brainwave signals, and obtaining second brainprint feature parameters;

Step 603, comparing the first brainprint feature parameter with the second brainprint feature parameter, and performing identification and security authentication on the user identity according to the comparison result.

The security authentication method based on brainprint collection in the method embodiment of the present invention also includes the following steps:

In the new user mode, receive the input of the new user identity information;

Obtain the brain wave digital signal generated by the new user under the stimulation of the video signal, perform feature extraction and analysis on the brain wave digital signal, obtain the new brain pattern feature parameter, and combine the new brain pattern feature parameter with the newly added After the user identity information is associated, it is stored in the brainprint local database.

Specifically, step 602 specifically includes the following steps;

Acquiring the analog brain wave signal generated by the user under the stimulation of audio and video signals, and amplifying, filtering, and analog-to-digital conversion of the analog brain wave signal to obtain a digital brain wave signal;

According to the brainprint feature parameters retrieved from the brainprint local database, feature extraction is performed on the brainwave digital signal to obtain the brainwave digital signal containing the brainprint feature parameters;

Analyzing and processing the brainwave digital signal containing the brainprint feature parameters to obtain the brainprint feature parameters, and correlating the brainprint feature parameters with the user identity information retrieved from the brainprint local database to obtain the corresponding According to the preset normal brain pattern characteristic parameters, it is judged whether the brain pattern characteristic parameters corresponding to the user are normal or abnormal. If normal, the brain pattern characteristic parameters corresponding to the user are the second brain pattern characteristic parameters. if abnormal, send the corresponding brainprint feature parameters to the preset abnormal brainprint information interface.

The security authentication method based on brainprint collection in the method embodiment of the present invention also includes the following steps:

Intercept part of the data from the brain wave digital signal, and compress it to obtain a batch of brain wave digital signals;

Clustering and processing the received batches of brainwave digital signals of the same user to obtain the new brainprint feature parameters of the user;

The new brainprint feature parameters are received and fed back to the local brainprint database, so that the brainprint local database updates the brainprint feature parameters corresponding to the user.

Specifically, the clustering and processing of the received batches of brainwave digital signals of the same user to obtain the new brainprint feature parameters of the user specifically includes the following steps;

Clustering the received batches of brainwave digital signals of the same user to obtain the brainwave parameter clustering result;

Based on the implicit correlation between known features of brain waves and brain wave parameters, new brainprint feature parameters are obtained according to the clustering results of brain wave parameters.

Specifically, step 603 includes the following steps:

Comparing the first brainprint feature parameter with the second brainprint feature parameter to obtain a comparison result;

According to the comparison result, it is judged whether the user identity is an authorized user or an unauthorized user, if the similarity between the first brainprint characteristic parameter and the second brainprint characteristic parameter of the user is within an acceptable range, then the user is an authorized user; if the user The similarity between the first and second brainprint feature parameters of the user is within an unacceptable range, or the user's second brainprint feature parameters do not match all the brainprint feature parameters in the local brainprint database, then the user as an unauthorized user;

Authorized users are granted permission, visitor intrusion warnings are given to unauthorized users, and the second brainprint feature parameters are stored in the preset non-real-name-authenticated brainprint sub-database.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims (8)

1. a kind of security certification system based on the acquisition of brain line characterized by comprising

Data access module, in conjunction with preset brain line local data base, is generated and is used for receiving the input of subscriber identity information Identity information corresponding first brain line characteristic parameter in family is stored with several subscriber identity informations in the brain line local data base And brain line characteristic parameter corresponding with the subscriber identity information；

Brain line generation module, the brain wave digital signal generated under the stimulation of audio-video signal for obtaining the user are right The brain wave digital signal carries out feature extraction and analysis, obtains the second brain line characteristic parameter；

Security authentication module is tied for being compared the first brain line characteristic parameter with the second brain line characteristic parameter according to comparing Fruit carries out identification and safety certification to user identity；

The brain line generation module includes acquiring brain waves submodule, brain wave feature extraction submodule and brain line analysis Module；

The acquiring brain waves submodule, the brain wave analogue letter generated under the stimulation of audio-video signal for obtaining user Number, and brain wave analogue signal amplification, filtering, analog-to-digital conversion are obtained into brain wave digital signal；

The brain wave feature extraction submodule, for according to the brain line feature ginseng transferred from the brain line local data base Number, to the brain wave digital signal carry out feature extraction obtain include brain line characteristic parameter brain wave digital signal；

The brain line analyzes submodule, for it is described include that the brain wave digital signal of brain line characteristic parameter carries out at analysis Reason, obtains brain line characteristic parameter, the brain line characteristic parameter and the subscriber identity information transferred from brain line local data base into Brain line characteristic parameter corresponding to the user is obtained after row association, and is judged described according to preset normal brain activity line characteristic parameter and used The corresponding brain line characteristic parameter in family is normal or abnormal, and if normal, then the brain line characteristic parameter corresponding to the user is the The second brain line characteristic parameter is sent to the security authentication module by two brain line characteristic parameters；It, will be with user if abnormal Corresponding brain line characteristic parameter is sent in preset abnormal brain line information interface.

2. security certification system as described in claim 1, which is characterized in that

Data access module is also used under the mode of Adding User, when receiving the input for the identity information that Adds User, triggering The brain line generation module；

The brain line generation module is also used to obtain the brain wave number for Adding User and generating under the stimulation of sound audio-video signal Signal carries out feature extraction and analysis to the brain wave digital signal, newly-increased brain line characteristic parameter is obtained, by the newly-increased brain Line characteristic parameter is stored in brain line local data base after being associated with the identity information that Adds User.

3. security certification system as claimed in claim 1 or 2, which is characterized in that the security certification system further includes brain line Batch processing module, brain line feature and identity information cloud database module:

The brain wave feature extraction submodule is also used to the brain wave digital signal acquired in the acquiring brain waves submodule Middle interception partial data is compressed and is exported to the brain line batch processing module；

The brain line batch processing module, the brain wave digital signal for the batch to the same user received cluster And processing, obtain the new brain line characteristic parameter of the user；

The brain line feature and identity information cloud database module, for receiving the new brain line characteristic parameter, and feed back Into brain line local data base, it is updated the brain line local data base to the corresponding brain line characteristic parameter of the user.

4. security certification system as claimed in claim 3, which is characterized in that the brain line batch processing module includes brain wave Cluster submodule, brain wave feature extraction and enhancing submodule；

The brain wave clusters submodule, and the brain wave digital signal for the batch to the same user received is gathered Class obtains brain wave Parameter Clustering result；

The brain wave feature extraction and enhancing submodule, for based on hidden between brain wave known features and brain wave parameter Containing incidence relation, new brain line characteristic parameter is obtained according to brain wave Parameter Clustering result.

5. security certification system as claimed in claim 1 or 2, which is characterized in that the security authentication module includes comparer Module, identification submodule and safety certification submodule；

The comparison submodule is compared for the first brain line characteristic parameter to be compared with the second brain line characteristic parameter As a result；

The identification submodule, for according to comparison result, judging that user identity is authorized user or unauthorized user, If the similarity of the first brain line characteristic parameter of user and the second brain line characteristic parameter is within an acceptable range, which is Authorized user；If the similarity of the first brain line characteristic parameter of user and the second brain line characteristic parameter is in unacceptable range Interior or user the second brain line characteristic parameter is mismatched with brain line characteristic parameters all in brain line local data base, then should User is unauthorized user；

The safety certification submodule, will to unauthorized user progress visitor's attack early warning for giving the open permission of authorized user The second brain line characteristic parameter is stored in preset non-real-name authentication brain line subdata base.

6. a kind of safety certifying method based on the acquisition of brain line, which comprises the following steps:

The input for receiving subscriber identity information generates corresponding with subscriber identity information in conjunction with preset brain line local data base First brain line characteristic parameter, be stored in the brain line local data base several subscriber identity informations and with the user identity The corresponding brain line characteristic parameter of information；

The brain wave digital signal that user generates under the stimulation of audio-video signal is obtained, the brain wave digital signal is carried out Feature extraction and analysis obtain the second brain line characteristic parameter；

First brain line characteristic parameter is compared with the second brain line characteristic parameter, user identity is known according to comparison result Other and safety certification；

The brain wave digital signal for obtaining user and being generated under the stimulation of audio-video signal, to the brain wave digital signal Feature extraction and analysis are carried out, the second brain line characteristic parameter is obtained and specifically includes following steps；

The brain wave analogue signal that user generates under the stimulation of audio-video signal is obtained, and the brain wave analogue signal is put Greatly, filtering, analog-to-digital conversion obtain brain wave digital signal；

According to the brain line characteristic parameter transferred from the brain line local data base, feature is carried out to the brain wave digital signal Extract obtain include brain line characteristic parameter brain wave digital signal；

To it is described include that the brain wave digital signal of brain line characteristic parameter is analyzed and processed, obtain brain line characteristic parameter, institute State brain line characteristic parameter be associated from the subscriber identity information transferred in brain line local data base after obtain it is corresponding with user Brain line characteristic parameter, and the brain line characteristic parameter corresponding to the user is being judged just according to preset normal brain activity line characteristic parameter Often or abnormal, if normal, then the brain line characteristic parameter corresponding to the user is the second brain line characteristic parameter；If abnormal, Then brain line characteristic parameter corresponding to the user is sent in preset abnormal brain line information interface.

7. safety certifying method as claimed in claim 6, which is characterized in that further comprising the steps of:

Under the mode of Adding User, the input for the identity information that Adds User is received；

The brain wave digital signal generated under the stimulation of audio-video signal that Adds User is obtained, to the brain wave digital signal Feature extraction and analysis are carried out, newly-increased brain line characteristic parameter is obtained, by the newly-increased brain line characteristic parameter and the identity that Adds User It is stored in after information association in brain line local data base.

8. safety certifying method as claimed in claims 6 or 7, which is characterized in that further comprising the steps of:

Partial data is intercepted in brain wave digital signal, is compressed the brain wave digital signal for obtaining batch；

The brain wave digital signal of the batch of the same user received is clustered and handled, the new brain of the user is obtained Line characteristic parameter；

The new brain line characteristic parameter is received, and is fed back in brain line local data base, the brain line local data base pair is made The corresponding brain line characteristic parameter of the user is updated；

It is described that first brain line characteristic parameter is compared with the second brain line characteristic parameter, according to comparison result to user identity into Row identification and safety certification specifically includes the following steps:

First brain line characteristic parameter is compared with the second brain line characteristic parameter, obtains comparison result；

According to comparison result, judge that user identity is authorized user or unauthorized user, if the first brain line feature of user Within an acceptable range, then the user is authorized user for parameter and the similarity of the second brain line characteristic parameter；If the of user The similarity of one brain line characteristic parameter and the second brain line characteristic parameter is in unacceptable range or the second brain line of user is special Sign parameter is mismatched with brain line characteristic parameters all in brain line local data base, then the user is unauthorized user；

It gives authorized user open permission, visitor's attack early warning is carried out to unauthorized user, the second brain line characteristic parameter is deposited Enter in preset non-real-name authentication brain line subdata base.