KR20050044073A - Method of iris image recogntion and storing with characteristic syndrome pattern - Google Patents

Abstract

The present invention relates to an iris identification, storage, and authentication method for identifying and storing irises according to lesion characteristics of the iris. The present invention extracts the inner boundary of the iris code by the flattening-thinning operation, which is the traditional contour tracking method, and concentrates on the autonomic nervous system that includes most of the iris information. Extract iris images and classify the iris image with the same weight as the six division regions of the radial shape, and transform the iris code that adopts the optimal basis function and enhances the analysis of high frequency by the wavelet packet method. In addition, by applying the SVM technique using the DISTANCE VECTOR in the recognition method, the transition function in the time domain is added to a specific frequency band as a reference point in order to compare the differentiated iris codes and to compare the rotation parts of the patterns. A Public Key for Implementing Rotational Matching of Twisted Iris Patterns and in Storage and Authentication Methods Used to perform the encryption of the communication messages between the objects to help protect your personal information.

Description

METHOD OF IRIS IMAGE RECOGNTION AND STORING WITH CHARACTERISTIC SYNDROME PATTERN}

The present invention relates to an identification and storage method according to the characteristics of the lesion of the iris, and more specifically, to identify and store the iris according to the characteristics of the lesion of the iris, by giving the same weight per unit area for each group of lesions, the FRR of a specific person is increased The present invention relates to a method for identification and storage according to the lesion characteristics of the iris, which improves the factors and enables faster and more accurate personal authentication.

In general, several methods are known as biometric methods for recognizing or authenticating each individual from biological characteristics of a human. Among these methods are photographing a face or an eye and analyzing facial features, eye retinal blood vessel patterns, or eye iris patterns.

In recent years, there is an increasing demand for a reliable system for authenticating each individual, especially those who want to access a secure area or security system. Common such security systems include door access control, and are being incorporated into financial transactions. Many of these systems are used by a wide variety of people. These users often require fast and accurate authentication.

Techniques for accurately authenticating each individual using iris recognition are disclosed in US Pat. No. 4,641,349 to Flum et al. And US Pat. No. 5,291,560 to Dogman. The systems disclosed in these patents require an image of the eye that is sharp and precisely focused. In addition, it is necessary to derive optimized feature values based on these images, so that user retries, time delays, authentication failures do not occur, and high reliability can be maintained.

Accordingly, the present invention is to solve the above-mentioned conventional problems, by identifying and storing the iris according to the lesion characteristics of the iris, by giving the same weight per unit area, while improving the factors that increase the FRR of a specific person, The purpose of the present invention is to provide a method of identification and storage according to the characteristics of the lesion of the iris that enables faster and more accurate personal authentication.

In other words, the conventional system extracts the iris features in a batch, whereas the present technique is based on the 'unit group features' of the biological characteristics of the iris. With this in mind, this technology is introduced to achieve an iris recognition method that minimizes authentication delays and authentication failures.

In addition, the iris recognition method solves the inappropriate position search, time delay, etc. of the existing methods in the iris pupil search, the boundary line extraction, etc., which are called preprocessing.

In addition, in order to solve the rotation part of the iris pattern that is generally generated during iris recognition, the posture corrected data may be found by the rotation transition function. On the other hand, in order to securely store the iris data thus obtained, the purpose is to implement a highly reliable system by encrypting and storing in a smart card using a public key.

Identification and storage method according to the lesion characteristics of the iris according to the present invention for achieving the above object,

After receiving the image for iris recognition and undergoing the preprocessing process, it estimates the boundary of the pupil through the process of estimating the object forming the circle by applying the concept of energy function. An internal boundary extraction step of the iris cord following a thinning operation, employing a diffusion filter for the flattening operation, and a Sobel mask for the thinning operation;

In general, the focus of the recognition target to the autonomic neural ring that is concentrated in the important iris features, extracting the external boundary of the iris code to extract the external boundary of the iris code by applying an elastic body model;

The iris data extracted by the inner boundary and the outer boundary extraction are divided into six areas of the radial division based on traditional iridiology as the area to be analyzed. Distinguishing the iris image;

Converting the iris data of each divided region into an iris code by adopting an optimal basis function using a wavelet packet method and enhancing an analysis on high frequency;

Comparing the iris code with an iris code for personal identification, which is stored in advance, for personal identification;

Rotating the iris code in the iris code comparison to implement rotation matching by adding a transition function in a time domain to a specific frequency band of the iris code;

 When the iris code is stored, it is stored in a smart card using a public key. The iris code is characterized in that it consists of an encryption step of an iris code that defines a requirement for encrypting a communication message between each object constituting the iris recognition system.

Hereinafter, exemplary embodiments of the present invention will be described with reference to the accompanying drawings.

1 is a flow chart of the identification and storage method according to the lesion characteristics of the iris according to the present invention, Figures 2A and 2B is a view showing the six divisions of the radial form based on the iris, Figures 3A and 3B The iris image region partitioned along the figure is shown. 4 is a diagram illustrating the structure of a personal certificate stored in a smart card, and FIGS. 5A and 5B are a flowchart of a process of encrypting iris data through such a certificate, and FIG. 6 is a location on the mechanism where such a smart card is inserted. Figure is a diagram.

As shown in the figure, the present invention, the inner boundary extraction step of the iris code (S10), the outer boundary extraction step of the iris code (S20), the separation step of the iris image (S30), the conversion step of the iris code (S40) , The comparison step (S50) of the iris code, the rotation step (S60) of the iris code, and the encryption step (S70) of the iris code.

The internal boundary extraction step (S10) of the iris code applies an energy function concept to estimate the boundary of the pupil through the process of estimating the object of which the energy function forms a circle, and the flattening-thinning operation, which is a traditional contour tracking method. The diffusion filter is adopted for flattening and the Sobel mask is used for thinning.

The external boundary extraction step (S20) of the iris code concentrates on the autonomic neural ring, by applying an elastic body, and extracts the iris data by extracting the external boundary of the iris code through a flexible method.

In the step S30 of distinguishing an iris image, six distinct regions of a radial form based on traditional iris are used as an analysis target region, and the divided regions have a weight equal to one. With this configuration, the weights of the zones in which lesions occur are differently represented as result values, and thus, FRR generation due to a singular value of an iris code specific section can be suppressed in the method of iris recognition.

In addition, the step (S10) of distinguishing the suitable iris image may include a zone of 40 degrees, a zone of 25 degrees, a zone of 45 degrees, a zone of 45 degrees, and a zone of 4 of the six divided zones of the radial shape. It has an area of 40 degrees, the fifth zone has an area of 65 degrees, and the sixth zone has an area of 145 degrees, respectively.

In addition, the iris regions which are later created as the area distribution have different weights, and data extraction is performed by multiplying the weight matrix before binarization of each image as an item of data preprocessing before the iris image is collected.

In the step of converting the iris code (S40), the iris data divided into the six areas is adopted by the wavelet packet method, and the iris code is transformed by adopting an optimal basis function and enhancing the analysis of the high frequency.

The iris code comparison step (S50) compares the iris codes by applying the SVM technique implemented in the method of processing and recognizing the iris image for personal identification, and the rotation step (S60) of the iris code identifies a specific frequency band of the iris code. Posture correction is achieved by adding the transition function in the time domain as a reference point, and then rotation matching is implemented.

The encryption step (S70) of the iris code is a method of storing using a public key when storing the iris code, and defines a requirement for encrypting a communication message between each object constituting the iris recognition system.

Referring to the operation of the present invention configured in this way in more detail as follows.

In classifying the appropriate iris image in the image input step through the iris recognition device, the processing and recognition method of the iris image for personal identification proceeds with the primary image selection through the blink function, and then the highest quality among the 10 iris images. This outstanding iris image is selected and selected as the object of recognition.

Then, in the extraction of the inner boundary of the iris code, in order to extract the boundary of the pupil, in the conventional invention, a method of estimating the pupil with a dark part and adopting the boundary of the pupil as the inner boundary of the iris code is common.

However, the drawback of this invention is that in the process of estimating the pupil as a dark part, other dark parts (eyebrows, etc.) may be estimated, which may result in delay and failure in the overall process. Applying the concept of energy functions in pupil estimation adds a process to estimate the objects from which these energy functions form a circle.

In addition, it follows the flattening-thinning operation, which is a traditional contour tracking method, but adopts a diffusion filter for the flattening operation and a Sobel Mask for the thinning operation.

In general, the edge detector uses a Gaussian mask to remove the noise, and then performs an edge detection mask such as a Sobel mask. In the first of these two processes, the noise removal effect occurs and the contour is detected from the image where the noise is removed. Way.

The pixel elements forming the boundary line detected by the above method must first be grouped. Grouping is the grouping of connected pixel elements into their respective groups, which includes sorting in the order of concatenation.

However, it is common for this delay to occur in the grouping process, and this delay causes the overall iris recognition process to be delayed. Therefore, the diffusion filter is used as a preprocessing step to smooth the outline of the minute shape change of the iris image. Eliminating noise in the iris image is a good first step in thinning.

The diffusion filter results in a reduction in intensity mismatch of pixels where the feature intensities are well above or even below the intensities of the neighboring pixels. The size of the filter is chosen according to the characteristic scales of the desired image and noise features.

The diffusion filter is one of frequently used filters, and is used to soften an image of a part such as a face of a person. There's a wide variety of things, from keeping the overall sharpness and contrast to eliminating some details, to affecting the overall contrast or sharpness. It is similar in concept to 'soft focus'.

Diffusion filters are used in a way of sharp speckle to reduce noise while maintaining the edges of image features. This is obtained when the intensity of the nearby central pixel is replaced by the median of the nearby intensities. An easy way to determine the median value for a given neighborhood is to sort the pixels in order from the lowest intensity values to the highest intensity values and then find the intermediate value.

There are fast sorting programs available for this task. When the median filter is centered on the noisy pixel, its intensity is replaced by the median of the near intensities. The effect of the median filter is to replace pixel values that are significantly different from the intensities of other pixels in the vicinity by the median value, which in turn eliminates the isolated noise very effectively.

This operation preserves edge discontinuities because the imputation includes pixel intensity values that already exist in the vicinity, rather than new values, such as the average, as in the filter case. However, median filters typically require more intensive computation than the filters do. The result of the intermediate value filtering is the same regardless of whether the noise pixel has a much higher intensity than the rest of the neighborhood. That is, the result is set to the same near-medium value. This is a representation of the nonlinearity of the median filter and it is distinguished when compared to the behavior of Gaussian filters, which is a blurring method of contour boundary extraction of the existing invention. This diffusion filter is particularly effective at removing isolated pixel noise.

The resultant value is then put into a full-fledged energy function construction, and the radius R of the initial pupil is expressed by the following equation.

The radius R of the pupil is expressed by the following equation.

Equation 2 is represented by the equation of the circle, Denotes the center of the pupil, assumed to be a black object, and R reflects the size of the pupil. The energy function of the pupil, which serves as a decisive factor in finding the optimal pupil template, The energy function value depends on the pixels inside the pupil template. The energy function of the pupil template can be expressed as

Area represents the area of the entire area of the pupil, UBound is the upper boundary of the pupil and the lower boundary LBound of the pupil is defined as in Equations 4 and 5 below.

here, Denotes the number of pixels representing the area located at each point inside the area surrounded by the pupil in the pupil area image. In detail Operate separately and use it as information for determining position conversion, Is used as information on the conversion of pupil template size. Deform the template and calculate the energy function to find the position with the maximum energy function.

Based on the location of the pupil detected in the above step, the method of finding the boundary of the pupil is more accurate, and the flattening and thinning are performed. Gaussian filter is the oldest method to perform the planarization operation, there are some disadvantages. First, Gaussian smoothing not only reduces noise, but also blurs features with important information in the image, such as edges.

Therefore, increasing the scale may damage the edge, which is important information. In addition, the edge position may change when moving from high resolution to low resolution. Because of this problem, a smoothing method using gradients is used (Perona-Malik method). This method is based on Nonlinear Isotropic Diffusion Filter, which keeps the edge position precisely when the scale of the pattern is increased, but there is still a problem in contrast with the case where there is a lot of noise in the image. Unlike the Perona Malik method, the edge improvement method used in the present invention considers the directionality at the local edge. The same part as the direction of the edge makes a lot of diffusion, and the part perpendicular to the direction of the edge does a little diffusion. This method can achieve a good effect when there is a lot of noise in the image which was a problem of the P-M model.

The method proposed in the present invention extracts the edge by considering the direction of the edge as well as the contrast at the edge. Previous methods, such as nonlinear Isotropic Diffusion Filters, have some problems because they obtain iris images using only contrast. The first problem is that if the image is noisy, the correct edges cannot be extracted.

However, this problem can be improved by using the Regularized Nonlinear Isotropic Diffusion method. In the conventional Perona-Malik Model, only the gradient value at the pixel position is used. In normalized nonlinear diffusion, the gradient of the surrounding pixel is obtained when the gradient of the pixel is obtained. Use the average value. This can eliminate some of the noise and then somehow solve the noise problem by doing diffusion. The second problem is when the edge portion of the original image is noisy. If the edge portion is noisy, even if the edge is extracted, there is a problem of dislocation and the wrong edge is extracted.

Therefore, in the present invention, in contrast to diffusion in the edge portion in the conventional method (diffusion) is a method of doing a lot of diffusion (diffusion) in the portion parallel to the edge and a little diffusion (diffusion) in the portion across the edge Use

In order to implement the present invention, a diffusion tensor is used in place of a conventional scale diffusivit y. The diffusion tensor can be obtained from the eigen vectors s v1 and v2. v1 is made parallel to u as in Equation (6), and v2 is configured to be perpendicular to u as in Equation (7).

  v2 ㅗ ▽ u

Therefore, the eigen values λ1 and λ2 were selected to planarize at the portion parallel to the edges rather than at the portion across the edges. Eigen values are shown in equations (8) and (9).

λ2: = 1

According to the above method, the diffusion tensor matrix D can be obtained as shown in Equation (10).

In order to implement the above diffusion tensor matrix in the actual program, it is necessary to know exactly the meaning of v1 and v2. If a vector of Gaussian filtering of the original image is represented by (gx, gy), v1 in Equation (6) is a vector that makes the original image parallel to Gaussian filtering. It can be expressed as (gx, gy), and in Equation (7), since v2 must be perpendicular, the dot product of the vector of (gx, gy) and v2 is zero. Thus v2 can be represented by (-gx, gy). Since v1t and v2t are transpose matrices of v1 and v2, the diffusion tensor D can be rewritten as in Equation (11).

D in Equation (11) can be obtained through the diffusivity of Equation (12). There are two main differences between the present method and the Perona-Malik Model. First, the Perona-Malik Model performs diffusion with the original image, whereas the present invention performs diffusion with the image with Gaussian filtering in the original image. Secondly, the Perona-Malik Model uses a scaled valued diffusivity, but the method uses a diffusion tensor matrix to consider not only the contrast of the image but also the direction of the edge. .

Extracting the inner boundary of the iris code in the same manner as above, and extracting the boundary of the outer boundary, finding the boundary of the pupil and iris and the boundary of the sclera and iris, focusing on the autonomic nervous system, while applying the elastic body Implemented in image processing and recognition methods. The elastic model is modeled that the iris muscle is composed of an elastic body connected to the outer frame by a pin coupling, the elastic body can move with the constraint that it can be deformed only in the longitudinal direction and not in the direction perpendicular to the length. Under these conditions, the fixed tip of the elastic body can be rotated left and right because it is coupled with a pin joint, and the pupil is formed by the unfixed ends of the elastic body, and the vertical direction of the pupil boundary can be set as the axial direction of the elastic body. .

In the iris code classification step, the definition of the iris code is described first. As shown in FIGS. 2A and 2B, six distinct regions of a radial form based on traditional iridiology are used as analysis subject regions, but the divided regions are the same. Has a weight of one. However, as shown in FIGS. 3A and 3B, all six divided areas have different areas, and thus, even the uniform areas have different weights. That is, the occurrence of damage due to lesions between the iris patterns is limited to the feature values only in the region and does not bring about the deformation of the overall feature values. The results of this study are to minimize the factors that impair iris recognition due to lesion damage by most people. In addition, the feature point caused by the lesion is recognized as a new iris feature value, which causes a factor that can bring more discrimination to an individual's authentication.

The purpose of this invention is to use the data that does not have the same weight for the extraction of the existing iris data as the analysis area, and according to the characteristics of the lesions that each person has, as shown in iris science, a specific person has a factor of increasing the FRR. Its primary purpose is to improve this.

As shown in Figs. 3A and 3B, Zone 1 has an area of 40 degrees, Zone 2 has an area of 25 degrees, Zone 3 has an area of 45 degrees, Zone 4 has an area of 40 degrees. The fifth zone has an area of 65 degrees and the sixth zone has an area of 145 degrees. With this area distribution, later iris areas are given different weights.

After the iris image is collected, data extraction is performed by multiplying the weight matrix before digitizing each image as an item of data preprocessing before feature point extraction.

The size of the iris data does not always match because the size of the image varies depending on the user's classification or the registration and recognition distance even for the same user. Therefore, this inconsistency is equalized after normalizing the size as follows. The entire eye-shaped image, including the iris, can be imaged at 640 × 480 pixels, of which the iris area is normalized to 360 × 40 pixels.

Therefore, the iris regions of the lesion division are divided as follows. That is, the first section has a 40 × 40 matrix, the second section has a 25 × 40 matrix, the third section has a 45 × 40 matrix, the fourth section has a 40 × 40 matrix, the fifth section has a 65 × 40 matrix, and the last 6 The intervals are divided into 145 x 40 matrices. Therefore, the extracted iris data is divided into six matrix values, each matrix is multiplied by a weight of 1, and the sum of all matrices is obtained to obtain a 360 × 40 matrix, which is the final raw data matrix.

These Pixels are transformed into data matrices with 45x5 byte values, each corresponding to one bit. The value of this matrix is representative of the final raw data, and the size of the raw iris data is finalized to 225 bytes.

The conversion of the iris code is implemented in a method of processing and recognizing an iris image for personal identification, but adopts an optimal basis function as a wavelet packet method and enhances analysis of high frequency.

The region including the low frequency component (LL) and the high frequency component (HH) is extracted from the divided images on the X and Y axes. Thereafter, the determination rate (E) or the number of repetitions of the process is calculated by calculating the energy values of the LL and HH areas. When the energy value (E) is less than or equal to a predetermined threshold, the process is terminated or repeated. The process ends even when is greater than the specified number of times. If the energy value E is equal to or greater than the threshold or the number of times the process is repeated is less than or equal to the specified number of times, the wavelet is stored by providing information of the corresponding area and providing the corresponding area as a new processing target image. The process is repeated again from the process of splitting the image into multiples by applying packet transformation. The HH and LL regions are selected from among the wavelet packet transforms in several stages, and are divided into N × N local windows to obtain the mean value and the standard deviation value. Construct a feature vector of.

In the comparison step of the iris code, an SVM technique implemented in a method of processing and recognizing an iris image for personal identification is applied. In other words, by receiving the feature vector and applying the Support Vector Machines classification rules, the individual is authenticated based on the determined result.

The rotation step of the iris code implements rotation matching by adding a transition function in the time domain with respect to a specific frequency band of the iris code. That is, the transition function in the time domain is added to rotate the iris code. This task can be expressed as convolution in the frequency domain.

The conventional method used to implement the rotation method of the iris code is to continuously compare the iris data with the shift (Shift) to a specific angle, but in the present invention, the previously measured gaze vector is used to transition to the current time point. Use an algorithm that In order to correct the posture with the concept of the gaze vector, a recursive algorithm for implementation is proposed after reinducing the viewpoint determination method and performing the mutual dispersion analysis.

About the eye vector, from A set of gaze vectors obtained within a finite window can be considered. At this time, To Unit eye vector measured in fuselage coordinate system at, If is the corresponding unit gaze vector calculated in the inertial coordinate system, the pose optimization problem is to find the optimal pose that minimizes the following loss function.

here Is the weight assigned to each eye vector, Is the weight assigned to each set of eye vectors, Is the rotational transition matrix, Is the optimal pose matrix. Expanding and arranging Equation 13 can be expressed as follows.

here Wow Is defined as

here Is a window attitude shape matrix defined as follows.

In equation (14) Is a constant, so the optimal pose matrix is To maximize to be.

This rotation transition function is performed in the following matrix form in an actual algorithm.

In posture correction, the posture matrix is mainly used. Equation 15 is expressed in a matrix form as follows.

here Is a symmetric matrix defined as

here Wow Is defined as

here Is of line, Represents an element of a column.

The problem of maximizing the attitude control value of Equation 17 is a parameter optimization problem with constraints. Therefore, the function including constraints is

here Is a Lagrange multiplier. The requirements for maximization are as follows.

From this the optimal rotational transition matrix must satisfy the following conditions.

Since the pose shape matrix is composed of a set of gaze vectors, the proposed algorithm needs space to temporarily store these sets.

Finally, the pre-posture matrix and the post-posture matrix are defined as follows.

Using the characteristics of the rotation transition matrix, the pre-position shape matrix and the post-position shape matrix have the following relationship.

here Is an attitude increment matrix expressed as

Where given By appropriately designing the forgetting coefficient for In this case, the posture increment matrix is approximated as follows.

Pre- and post-information matrix of iris code location are defined as follows.

Similarly, using the characteristics of the transition matrix, the pre-information matrix and the post-information matrix have the following relationship.

here Is an incremental information matrix, approximated as

Finally, the rotation transition matrix for attitude control is calculated as follows.

The encryption step of the iris code is a method of storing the iris code using a public key to define requirements for encrypting communication messages between units that constitute the iris recognition system. In the present invention, encryption is performed by storing and retrieving a certificate through a smart card.

Since the purpose of this encryption is applied to the message transmission part between each object of the iris recognition system, it can secure security and safety from attacks such as illegal access and secure reliability of 1: 1 comparison based on the verification identification. have.

The public key method of the encryption method of the present invention is implemented as follows through the structure shown in FIG.

1) The smart card is inserted into the card reader of the inlet 100 of FIG.

2) Set the access control password for the smart card.

3) Enter user personal information.

4) Complete the user's iris registration and save it to the smart card.

5) Generate a public / private key pair within the smart card (store the private key in a secure place where external access is blocked).

6) Issuing a personal certificate by signing data combining the user's personal information and public key with the private key

7) The personal certificate is stored inside the smart card.

8) Register the personal certificate to the directory service.

Such encryption includes contents of an initial key distribution method of each individual entity, a key update method of each entity, and the like. More specifically, how to store and use iris data

(Key issuance process)

① Issuance of private key / public key

② Save-Smart Card

(Encryption Biometric Data Card Issued)

③ Registering iris (bio) data acquisition

④ Private key acquisition smart card

⑤ Encryption

⑥ Storage (Encryption Biometric Data)-Smart Card

(Use of iris data)

⑦ Public key acquisition-smart card

   Encryption Biometric Data Acquisition-Smart Card

⑧ Real-time biometric data acquisition recognizer

⑨ ⑦ Password Data Decryption

⑩ matching

This is done through the process of.

Although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the above-described embodiments, and the present invention is not limited to the above-described embodiments without departing from the spirit of the present invention as claimed in the claims. Various modifications can be made by those skilled in the art, and such modifications are intended to fall within the scope of the appended claims.

As described above, according to the identification and storage method according to the lesion characteristics of the iris according to the present invention, the iris is identified and stored according to the lesion characteristics of the iris, but by giving the same weight per unit area, a factor that increases the FRR of a specific person In addition to improving, there is an effect that allows for faster and more accurate personal authentication. In addition, in extracting the iris boundary to extract the iris region, an accurate boundary region is established by using an energy function, a Diffusion filter, and an elastic body model, and the rotation part is monitored through the rotation transition function for comparison of the iris data. In addition, by lowering the FAR, the secured iris data can be stored in the smart card using the public key, thereby implementing a secure system process.

1 is a flow chart of the identification and storage method according to the lesion characteristics of the iris according to the present invention.

2A and 2B show six distinct regions of radial form based on iris.

3A and 3B show iris image regions segmented in accordance with the present invention.

4 illustrates a certificate storage structure through a public key.

5A and 5B are flowcharts illustrating a method of storing and authenticating iris data by classifying a register and a recognizer through a public key.

6 shows the smart card inlet of the iris mechanism;

Claims (7)

In order to extract the iris from the input image for iris recognition, the concept of energy function is applied to estimate the boundary of the pupil through the process of estimating the object forming the circle, and the traditional contour tracking method An internal boundary extraction step of the iris cord following the linearization operation, employing a diffusion filter for the planarization operation, and employing a Sobel mask for the thinning operation; Focusing on the autonomic ring in addition to the internal boundary extraction, extracting the external boundary of the iris code to extract the external boundary of the iris code by applying an elastic body; The iris image obtained by the internal boundary and the external boundary extraction is divided into six areas of analysis based on traditional iris science as the analysis target area, and the iris data is divided into six matrix values, and the same weight 1 is applied to each matrix. Distinguishing a suitable iris image by obtaining a matrix sum after multiplication to obtain an iris data matrix for iris recognition; A step of converting the iris code divided into iris data into the iris code by adopting an optimal basis function by wavelet packet method and enhancing the analysis of high frequency; Comparing the iris code with an iris code previously stored for personal identification; Rotating the iris code in the iris code comparison to implement rotation matching by adding a transition function in a time domain to a specific frequency band of the iris code; A method of storing an iris code by using a public key, which comprises an encryption step of an iris code that defines a requirement for encrypting a communication message between each object of an iris recognition system. Identification and storage methods accordingly. The method of claim 1, wherein the distinguishing of the suitable iris image comprises Of the six radial divisions, one zone has an area of 40 degrees, the second zone has an area of 25 degrees, the third zone has an area of 45 degrees, the fourth zone has an area of 40 degrees, and the fifth zone is 65 And the sixth zone has an area of 145 degrees, respectively. The method of claim 1, wherein the distinguishing of the suitable iris image comprises The iris regions to be later generated as the area distribution have different weights, and data extraction is performed by multiplying the weight matrix before binarization of each image as an item of data preprocessing before the iris image is collected. Identification and storage method according to the characteristics of the lesion of the iris. The method of claim 1, wherein the extracting the inner boundary of the suitable iris image comprises: In the pupil area image, the area located at each point inside the area surrounded by the pupil is used as the information for determining the position transformation, and the pupil template is deformed based on the information on the transformation of the pupil template size, and the energy function is calculated A method for identification and storage according to lesion characteristics of an iris characterized by detecting a location having an energy function. The method of claim 1, wherein the extracting the inner boundary of the suitable iris image comprises: In consideration of the directionality at the local edge, the same part as the direction of the edge makes a lot of diffusion, and the part perpendicular to the direction of the edge makes the diffusion a little. Identification and storage method according to the lesion characteristics of the iris, characterized in that extracting the edge in consideration of the direction of. The method of claim 1, wherein rotating the suitable iris cord A method for identifying and storing an iris according to lesion characteristics, characterized by reinducing the viewpoint determination method and performing a mutual dispersion analysis in order to correct a posture with the concept of a gaze vector used by shifting the measured gaze vector to a registration time point. The method of claim 1 wherein the step of encrypting the suitable iris code comprises: When storing the iris code, the public key is used to store the iris code.The iris is characterized by using communication message encryption between each unit constituting the iris recognition system and storing and retrieving the certificate through a smart card. Method of identification and storage according to lesion characteristics.