WO2019223068A1 - Iris image local enhancement method, device, equipment and storage medium - Google Patents

Abstract

Disclosed in the invention are an iris image local enhancement method, device, equipment and a storage medium. The iris image local enhancement method comprises steps of: acquiring an iris image set; calculating contrasts of iris images and sorting the iris images according to a sequence from a large contrast to a small contrast to obtain an initial iris sequence; obtaining a preset number of iris images from the initial iris sequence according to the sequence from a large contrast to a small contrast to form an initial iris set; carrying out local enhancement processing on initial iris images by using an optimized contrast algorithm to obtain a first enhanced iris image set; and sharpening first enhanced iris images by using a Laplacian operator to obtain a second enhanced iris image set. According to the iris image local enhancement method, the overall contrast of the initial iris images is improved, internal details are enhanced, highlight generated during an enhancement process is suppressed, the enhancement effect is good, and the recognition accuracy of the iris images is improved.

Description

Method, device, equipment and storage medium for local enhancement of iris image

This application is based on a Chinese invention patent application filed on May 25, 2018 with the application number 201810511986.7, entitled "Method, Device, Device, and Storage Medium for Partial Enhancement of Iris Images", and claims priority.

Technical field

The present application relates to the field of image processing, and in particular, to a method, a device, a device, and a storage medium for locally enhancing an iris image.

Background technique

As an important identification feature, iris has the characteristics of uniqueness, stability, collectability and non-invasiveness. In iris recognition systems, high-definition iris images are often used as training sets, but due to the limitations of acquisition equipment and changes in the acquisition environment, the quality of the acquired iris images will be poor, such as low contrast and noise interference. Such issues will affect the highlighting of iris texture features, and then affect the clarity and recognition efficiency of the iris image training set. In order to improve the accuracy of recognition, it is often necessary to enhance the iris image to highlight the texture features of the image. Currently, the overall contrast of the collected iris image is usually adjusted dynamically. However, the accuracy of the iris image processed in the recognition system is still not high.

Summary of the Invention

Based on this, it is necessary to provide a method, a device, a device, and a storage medium for local enhancement of an iris image in response to the above technical problems, in order to solve the problem of low accuracy of iris image recognition.

A local iris image enhancement method includes:

Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set.

An iris image local enhancement device includes:

An iris image set acquisition module, configured to obtain an iris image set, wherein the iris image set includes an iris image, and the iris image includes a user identifier;

The iris sequence acquisition module is used to calculate the contrast of the iris images in the iris image set, and sort the iris images corresponding to each user ID in the iris image set in the order of the contrast, to obtain the corresponding Initial iris sequence;

The initial iris set acquisition module is used to obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

A first enhanced iris image set acquisition module, configured to perform local enhancement processing on the initial iris image in the initial iris set using an optimized contrast algorithm to obtain a first enhanced iris image set;

A second enhanced iris image set acquisition module is configured to sharpen the first enhanced iris image in the first enhanced iris image set by using a Laplacian to obtain a second enhanced iris image set.

A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented:

Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set. One or more non-volatile readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the following steps:

Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set.

Details of one or more embodiments of the present application are set forth in the accompanying drawings and description below. Other features and advantages of the application will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions of the embodiments of the present application more clearly, the drawings used in the description of the embodiments of the application will be briefly introduced below. Obviously, the drawings in the following description are just some embodiments of the application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without paying creative labor.

FIG. 1 is an application scene diagram of an iris image local enhancement method according to an embodiment of the present application; FIG.

2 is a flowchart of a method for locally enhancing an iris image in an embodiment of the present application;

3 is a flowchart of a specific implementation of step S10 in FIG. 2;

4 is a flowchart of a specific implementation of step S20 in FIG. 2;

5 is a flowchart of a specific implementation of step S40 in FIG. 2;

6 is a flowchart of a specific implementation of step S50 in FIG. 2;

FIG. 7 (a) is an example diagram of an initial iris image in the embodiment of the present application; FIG.

7 (b) is an example diagram of a second enhanced iris image in the embodiment of the present application;

8 is a schematic diagram of an iris image local enhancement device according to an embodiment of the present application;

FIG. 9 is a schematic diagram of a computer device in an embodiment of the present application.

Detailed ways

In the following, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

The local enhancement method of the iris image provided in the present application can be applied in a computer device or system to enhance the iris image to solve the problem of low recognition accuracy of the iris image. Among them, the computer device may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.

Optionally, if the iris image local enhancement method is applied in a system, the system may include a server and a client. FIG. 1 shows an application scene diagram of the local enhancement method of the iris image applied in the system. Among them, the server and the client are connected through the network, the client collects or obtains the iris image, and the server obtains the iris image from the client. The client can be a camera, camera, scanner, or other device with a photographing function. (Phone, tablet, etc.), or a storage device that stores iris images. There can be one or more clients. The server can be implemented by a server or a server cluster composed of a plurality of servers.

In an embodiment, as shown in FIG. 2, a method for locally enhancing an iris image is provided. The method is applied to a computer device as an example for description, and includes the following steps:

S10: Acquire an iris image set. The iris image set includes an iris image, and the iris image includes a user logo.

The iris image set refers to an image set composed of iris images, and the iris image refers to an image obtained by photographing an iris inside a user's eye through a camera device. Optionally, the iris image set may be acquired in real time, or may be stored in a computer device in advance. An iris image set may include iris images of one user, and may also include iris images of multiple users. For example, an iris image set includes iris images of N users. If each user has M iris images, then the iris image set has M * N iris images. Preferably, the iris image set includes at least two iris images. The user logo refers to the logo of the user to which the iris image belongs, and is used to classify the iris image according to the user. Each iris image corresponds to a user logo, and the iris image of the same user corresponds to the same user logo.

In a specific embodiment, a predetermined number of iris images can be acquired by photographing the eyes of the user to form an iris image set, or a predetermined number of pre-stored iris images can be acquired from a computer device to form an iris image set, or acquired by photographing the eyes of the user Part of the iris image, and another part of the pre-stored iris image is obtained from the computer equipment, and the two together constitute the iris image set.

Preferably, multiple iris images of several users can be selected as the iris image set during the same time period. For example, it can be collected at noon on a rainy day or in the afternoon on a sunny day. This can avoid the problem that the contrast of the iris images in the iris image set that are acquired is greatly different due to light changes.

S20: Calculate the contrast of the iris images in the iris image set, and sort the iris images corresponding to each user ID in the iris image set in the descending order of the contrast to obtain the initial iris sequence corresponding to each user ID.

Among them, contrast is an index to measure image quality. Specifically, the contrast of an iris image is the ratio of black and white of the image, which is used to represent the gradation of the iris image from black to white. The larger the ratio, the more the gradation of the iris image from black to white, and the richer the color expression. The effect of contrast on visual effects is very critical. Generally speaking, the larger the contrast, the clearer and sharper the image, and the brighter and more vivid the colors. The high-contrast iris image has more obvious advantages in detail performance, sharpness, and high-speed moving object performance in some dark scenes.

The initial iris sequence refers to an iris sequence in which the iris image corresponding to each user's logo is arranged in order of increasing contrast. The iris sequence corresponding to the iris image corresponding to each user ID is arranged in descending order of contrast as a sub-iris sequence corresponding to the user ID. Specifically, the contrast calculation is performed on the multiple iris images of each user identifier one by one, and the multiple iris images corresponding to each user identifier are sorted according to the order of the contrast from large to small, to obtain the sub-substance corresponding to each user identifier. Iris sequence. The larger the contrast, the higher the sharpness of the iris image. Therefore, the iris images corresponding to each user's logo in the iris image set are arranged in order of increasing contrast. For example: there are N user IDs in the initial iris sequence, and each user ID includes M iris images, then there are N sub-iris sequences, and these N sub-iris sequences together form the initial iris sequence, and the iris in each sub-iris sequence The images are arranged in order of increasing contrast.

It is easy to understand that because contrast is an important indicator of image quality, you can calculate and compare the contrast of each iris image, and sort the iris images in the iris image set in descending order of contrast, and then An iris image with a large contrast is used as a criterion for selecting an iris image, which is convenient for subsequent selection of an iris image with a high contrast for processing to obtain an iris image with more texture characteristics.

S30: Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID according to the order of increasing contrast, and form an initial iris set.

The preset number is a preset value, which is used to select a certain number of iris images for subsequent enhancement processing. Optionally, the preset number may be set according to a requirement of a training sample size. For example, if the number of training samples required for each user identification is P in subsequent model training, a preset number can be set to P. Optionally, a preset number of values may be set according to the number of iris images corresponding to each user identifier. For example, in the case of 30 images collected by each user's logo, the corresponding preset number can be set to 10, that is, only the 30 iris images need to be displayed in the order of the contrast in each user logo. In the corresponding initial iris sequence, 10 iris images can be selected as the initial iris set from large to small. The initial iris set is a set composed of a preset number of iris images with a contrast value selected from the initial iris sequence. The iris image with larger contrast is selected to form the initial iris set, thereby excluding iris images with lower contrast, reducing some redundant images, reducing the workload of subsequent iris image enhancement processing, accelerating the speed of iris image enhancement processing, and improving subsequent iris. Image processing efficiency. At the same time, because the iris image with a large contrast is selected as the initial iris set, the enhancement degree of the iris image can be improved, and the recognition rate of the iris image can be improved.

S40: Perform local enhancement processing on the initial iris image in the initial iris set using an optimized contrast algorithm to obtain a first enhanced iris image set.

Among them, Optimized Contrast Enhancement (OCE) is an algorithm that estimates the atmospheric light area A and the optimal transmittance t (x, y) based on the atmospheric scattering model, and then restores the image to enhance the contrast of the image. . The atmospheric scattering model expression is:

I (x, y) = t (x, y) J (x, y) + (1-t (x, y)) A;

In a specific embodiment, a layered search method is first used to search the bright pixels (pixels with a high gray value) of the initial iris image I (x, y) in the initial iris image set, and then the atmospheric light area A is obtained. The initial iris image I (x, y) is segmented. Assuming that the scene depth of each segment in the initial iris image is the same, find the optimal transmittance t (x, y) of the segment in the initial iris image. Finally, According to the estimated atmospheric light area A and the optimal transmittance t (x, y), the contrast of the original iris image I (x, y) is restored maximally to obtain a first enhanced iris image J (x, y). By optimizing the contrast algorithm based on the atmospheric scattering model, the loss of detail information of the initial iris image with low contrast is avoided, and the detail information of the initial iris image is better protected. The dark pixels (pixels with low gray values) of the initial iris image are well protected. ) Is also enhanced to a greater degree, so the initial iris image is sharper overall. Therefore, the first enhanced iris image set with rich and clear texture can be obtained to improve subsequent recognition accuracy. In this embodiment, the initial iris image in the initial iris set is the input of the optimized contrast algorithm, and the first enhanced iris image is the output of the optimized contrast algorithm. Based on the atmospheric scattering model, the dark pixels of the iris image have also been greatly enhanced, and the detailed information of the iris image is more abundant.

S50: The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian operator to obtain a second enhanced iris image set.

The first enhanced iris image refers to an iris image obtained by using an optimized contrast algorithm to enhance the initial iris image in the initial iris set. Laplacian operator is a second-order differential operator, which is suitable for improving image blur caused by diffuse reflection of light. The principle is that during the process of shooting and recording an image, light spots diffusely reflect light to its surrounding area. This diffuse reflection of light causes a certain degree of blurring in the image. The degree of blurring is relative to that of images taken under normal circumstances. It is said that it is often a constant multiple of the Laplace operator. Therefore, sharpening the Laplace operator on the image can reduce the blur of the image and improve the sharpness of the image. Therefore, by sharpening the first enhanced iris image, highlighting the edge detail features of the first enhanced iris image, and improving the contour definition of the first enhanced iris image.

Sharpening processing refers to the transformation of sharpening an image to enhance the target boundaries and image details in the image. The second enhanced iris image refers to an iris image obtained by performing a sharpening process on the iris image in the first enhanced iris concentration using a Laplacian operator. After the first enhanced iris image is sharpened by the Laplacian operator, the edge detail features of the image are enhanced, and the highlights in the first enhanced iris image are also suppressed, thereby protecting the details of the first enhanced iris image.

Optionally, the process of sharpening the first enhanced iris image by using a Laplacian may be: using a Laplacian to obtain a second derivative of a gray value of a pixel of the first enhanced iris image, and a second order The pixel corresponding to the derivative at zero is the edge pixel of the image. Such processing can more clearly show the edges of the iris texture, so as to obtain a clear iris training set with richer texture details and improve the recognition effect.

Further, when the enhanced contrast algorithm is used to enhance the initial iris image, since the contrast of the initial iris image is improved as a whole, the dark pixels of the initial iris image are enhanced and the bright pixels (higher gray values) are enhanced. Pixels) to enhance, so that the gray value of some bright pixels overflows and is over-enhanced, thereby generating highlight areas. For this reason, the Laplacian operator in the sharpening process is used to reduce the contrast of bright pixels, which can suppress the highlights in the first enhanced iris image. By combining the advantages of local enhancement processing and sharpening processing, combined with Laplace operator processing, the highlights generated during the local enhancement process by the optimized contrast algorithm are suppressed, making the second enhanced iris detail information richer.

In this embodiment, an iris image set is first obtained, a contrast calculation is performed on the iris image set in the iris image set, and an iris image with a high contrast in the iris image set is extracted to form an initial iris set, thereby reducing the iris image with poor quality and reducing The redundant operation is helpful to improve the enhancement degree of the iris image and the efficiency of subsequent enhancement processing. Then, the initial iris image in the initial iris set is subjected to local contrast enhancement processing using an optimized contrast algorithm, which improves the contrast of the initial iris image, and at the same time, the dark pixels of the initial iris image are also effectively enhanced. Finally, the enhanced first enhanced iris image is sharpened to suppress the highlights generated during the local enhancement process by the optimized contrast algorithm. At the same time, the second enhanced iris image is obtained after the sharpening process, retaining more details of the iris image. As a whole, the contrast of the iris image is improved, so that the texture features of the iris image are more clear, so that the iris training set with richer and clearer texture details is obtained, and the accuracy of subsequent recognition is improved.

In an embodiment, as shown in FIG. 3, in step S10, obtaining an iris image set includes the following steps:

S11: Obtain the measured distance between the human eye and the camera in real time. If the measured distance is not within the distance threshold, a prompt message is sent.

Among them, the measured distance refers to the distance between the user's eyes and the camera, and the distance threshold refers to a preset distance value given by repeated tests during the experimental measurement. If the subject is at the distance threshold, the data collected by the computer equipment The image quality is better than the images acquired at other locations. The distance threshold range refers to the limit set above and below the distance threshold. It is easy to understand that the measured distance can also capture a clear image within a certain range where the distance threshold fluctuates. Under the condition that the image quality is clear, in order to facilitate the fast shooting of the image, a range of the distance threshold ± a% can be set as the distance threshold range. Optionally, a can be 5, 10, 15, and so on. The prompt information is used to prompt the user to make corresponding adjustments in order to take a clear image. The prompt information includes, but is not limited to, arrow identification (such as arrows in different directions), text prompt information (such as too far, appropriate or close), and voice Information (such as "Please approach the camera", "Collecting" or "Please stay away from the camera").

After obtaining the measured distance, determine whether the measured distance is within the distance threshold. If it is not within the distance threshold, send a prompt message, and the user adjusts accordingly according to the prompt information, and then obtain the measured distance until the measured distance is within the distance threshold. By guiding the user within the range of the distance threshold, it is beneficial to improve the quality of the subsequently acquired iris image.

In a specific implementation, taking an infrared camera as an example, the focal length of the infrared camera can be used as the distance threshold. Specifically, the infrared camera can be manually or automatically adjusted to determine the focal length of the camera according to the sharpness of the image. .

In this embodiment, the corresponding prompt information is sent by comparing the measured distance and the distance threshold range, which can guide the user to quickly adjust the position and improve the efficiency of iris image collection.

S12: If the measured distance is within the distance threshold, the camera is controlled to perform continuous shooting to obtain an iris image set.

Understandably, when the distance between the user's eyes and the camera is within the distance threshold, in this case, the quality of the iris obtained by shooting is better. Continuous shooting can obtain multiple iris images of the same person, which is convenient and fast, and provides a better quality iris image set for subsequent enhancement processing.

In this embodiment, the measured distance between the user's eyes and the camera is obtained, and the measured distance is compared with the distance threshold range, and corresponding prompt information is fed back to the user according to the comparison result. The user adjusts according to the prompt information, and when the measured distance is within the threshold range Inside, the camera is controlled for continuous shooting to obtain the iris image, which can quickly and easily obtain the iris image, which also improves the quality of the iris image.

In an embodiment, as shown in FIG. 4, in step S20, the contrast of the iris image in the iris image set is calculated, and specifically includes the following steps:

S21: Obtain the gray value of each pixel of the iris image in the iris image set, and use each pixel as the central pixel in turn.

Among them, a pixel is a basic element of a digital image, and a pixel is obtained by discretizing a continuous space when an analog image is digitized. Each pixel has integer row (height) and integer column (width) position coordinates, while each pixel has integer grayscale or color values. An image is made up of many pixels. Specifically, digital image data can be represented by a matrix, so matrix theory and matrix algorithms can be used to analyze and process digital images. The pixel information of a grayscale image is a matrix, the rows of the matrix correspond to the height of the image, the columns of the matrix correspond to the width of the image, and the matrix elements correspond to the pixels of the image. The value of the matrix element is the grayscale value of the pixel, which represents the grayscale image. The depth of the color. Specifically, a gray value corresponding to each pixel of the iris image can be obtained through an image information acquisition tool. That is, the path corresponding to the image is given, and the image under the path is read through the path. For example, this can be achieved with the imread function:

I = imread ('D: \ lena.jpg');

Among them, jpg is the format of the image, lean is the name of the image, "D: \" is the path of the lean image, and I is the matrix corresponding to the lean image. The center pixel is the pixel located at the center in a given area. In this implementation, sequentially referring to each pixel as the center pixel means that in a given area, each pixel in the area is regarded as the center pixel. For example, if there are 15 pixels in the area, and these 15 pixels are used as the center pixels, then there are 15 center pixels. When the boundary pixel is the center pixel, the boundary pixel can be regarded as the center pixel by extending the pixel, that is, the gray value of a pixel that does not exist in the neighborhood of the boundary pixel is set to be equal to the gray value of the boundary pixel. For example, the matrix of an iris image is:

Among them, the gray value of the pixels in the first row and the first column is 22, and there are no pixels in the left and upper parts. When calculating the contrast, the gray values of the left and upper pixels are set to the boundary. The gray value of the same pixel size, that is, the gray value of the left and upper parts are both 22.

S22: Calculate the difference between the gray value of each center pixel and the gray value of the corresponding neighborhood pixel according to the preset neighborhood size.

Among them, the neighborhood pixel refers to the pixel adjacent to the center pixel position. For example, the pixel p at the coordinate (x, y) has two horizontal and two vertical adjacent pixels, and each pixel distance (x, y) One unit distance. The coordinates are: (x-1, y), (x + 1, y), (x, y-1), (x, y + 1). This pixel set is defined as the four neighborhoods of pixel p, which is represented by N4 (p). In addition, pixel p has 4 diagonally adjacent pixels with coordinates: (x-1, y-1), (x + 1, y-1), (x-1, y + 1), (x + 1, y + 1). These four diagonally adjacent pixels and N4 (p) are collectively referred to as the 8-neighborhood of pixel P and are represented by N8 (P).

If the default neighborhood size is 4, that is, the neighborhood of 4 is taken, then the difference between the gray value of each central pixel and the pixel of the corresponding neighborhood is 4. If the gray value of the central pixel is h (x, y) Display, then the difference between the gray value of the pixel and the corresponding pixel in the 4 neighborhoods can be obtained by the following formula:

q ₁ = h (x-1, y) -h (x, y);

q ₂ = h (x, y-1) -h (x, y);

q ₃ = h (x + 1, y) -h (x, y);

q ₄ = h (x, y + 1) -h (x, y);

It is easy to understand that when the central pixel is a boundary pixel, at least one of the differences q ₁ , q ₂ , q ₃ , and q ₄ of the corresponding gray value is 0.

S23: Obtain the number of differences in gray values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image.

For example, let the matrix corresponding to an iris image be

Then the number of rows m = 3 and the number of columns n = 5 of the matrix M. It is easy to understand that through the image information acquisition tool, the matrix corresponding to the iris image can be acquired, and then the number of rows and columns of the matrix can be acquired.

If the preset neighborhood size is 4, and the number of rows and columns of the matrix is m and n, respectively, the number k of differences between gray values can be obtained by the following formula:

k = 4 × (m-2) × (n-2) + 3 × (2 × (m-2) + 2 × (n-2)) + 4 × 2;

If the preset neighborhood size is 8, and the number of rows and columns of the matrix is m and n, respectively, the number k of differences between gray values can be obtained by the following formula:

k = 8 × (m-2) × (n-2) + 6 × (2 × (m-2) + 2 × (n-2)) + 4 × 3.

S24: Sum the difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighboring pixel, and divide it by the number of differences in the gray value in the iris image to obtain the iris image. Contrast.

Among them, the contrast of the iris image is represented by C, and the difference between the gray value of each central pixel and the gray value of the corresponding neighboring pixel in the iris image is q ₁ , q ₂ … q _k , and k is a positive integer. The specific calculation formula for the contrast C of the iris image is as follows:

C = (q ₁ ² + q ₂ + ... + q _k ² ) / k;

As can be seen from the formula, the contrast C is a specific value.

In this embodiment, first, the gray value of each pixel of the iris image is obtained, and each pixel is sequentially used as the center pixel. According to the preset neighborhood size, the gray value of the center pixel and the gray value of the preset neighborhood pixel are calculated Degree difference, the number of gray value differences is calculated by presetting the size of the neighborhood and the number of rows and columns of the corresponding matrix of the iris image, and then comparing the gray value of each central pixel in the iris image with The gray value difference corresponding to the neighboring pixels is squared and summed and then divided by the number of gray value difference values. The obtained result is the contrast of the iris image. Through steps S21 to S24, the contrast of the iris image can be calculated simply and quickly, and a high-quality iris image can be filtered out by comparing the contrast.

In an embodiment, in step S40, as shown in FIG. 5, an optimized contrast algorithm is used to perform local enhancement processing on the initial iris image in the initial iris set, which specifically includes:

S41: Calculate the atmospheric light area A and transmittance t (x, y) of the initial iris image in the initial iris set based on the atmospheric scattering model.

Among them, the atmospheric scattering model refers to a model established by forward scattering and backward scattering of the atmosphere, and is used for image restoration. The atmospheric light is regarded as a light source. For a fixed scene, the atmospheric light area can be considered as determined. Generally, the pixel with the largest gray value of the image is used as the atmospheric light area A. In a specific implementation, in order to avoid the largest pixel as the atmospheric light area A from adversely affecting the atmospheric light estimation, the variance of the gray value of the blurred area based on the iris image is relatively small. The layer search method estimates the atmospheric light area A. The specific method is: first divide the initial iris image into four rectangular regions of the same size from the center, calculate the difference between the mean value and the standard deviation of the gray values of each rectangular region of the initial iris image in order, and select the region with the larger difference Until the size of the rectangular area is smaller than the preset value, then select the rectangular area and use the formula d = | (I _r (x, y), I _g (x, y), I _b (x, y))-(255,255,255 ) | Take r, g, b of the pixel with the smallest gray value (r, g, b respectively represent the red component, green component and blue component in the RGB tri-color mode) The smallest component is used as the atmospheric light area A, in the formula , (X, y) are the coordinate values of the pixels of the initial iris image, I _r (x, y), I _g (x, y), I _b (x, y) are the red component, the green component, and the blue color, respectively The gray value of the component, d is the difference between the gray value of the rectangular area corresponding to the initial iris image and the preset rectangular area.

After the atmospheric light area A is estimated, the restoration of the initial iris image depends on the value of the transmittance t (x, y), so in a local sub-image block, the optimal transmittance is estimated by solving the maximum contrast value of the initial iris image t (x, y). In a specific implementation manner, the mean square error of gray values in a local area is used as a criterion for judging image contrast. Given the region B, its formula for calculating the optimal contrast is as follows:

Among them, E _contrast is the value of the optimized contrast corresponding to region B, and c∈ {r, g, b} is the index label of the color channel.

The pixel averages of J (x, y) and I (x, y) in local area B, respectively, and N _B is the number of pixel points in area B.

S42: Based on the atmospheric light area A and the transmittance t (x, y), the following formula is used to perform image restoration on the initial iris image:

Among them, (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the first output of the optimized contrast algorithm. An enhanced gray value of the iris image.

Among them, image restoration refers to using the prior knowledge of the degradation process to restore the original appearance of the degraded image. The specific realization of the restoration is achieved by the following formula (that is, the rewrite of the atmospheric scattering model formula),

In the formula, the transmittance t (x, y) is a fixed value, the atmospheric light area A is a fixed area (for example, 32 × 32), and I (x, y) is the gray of the initial iris image input for the optimized contrast algorithm. Degree value, J (x, y) is J (x, y) is the gray value of the first enhanced iris image output by the optimized contrast algorithm, that is, the gray value of the restored iris image.

In this embodiment, the atmospheric light area A and transmittance t (x, y) of the iris image in the first enhanced iris image set are calculated based on the atmospheric scattering model, and then the initial iris image is restored to obtain the first enhanced iris image. The optimized contrast algorithm enhances the initial iris image, so that the contrast of the initial iris image is improved. Based on the atmospheric scattering model, the detailed information of the iris image is also protected, and the clarity of the first enhanced iris image is improved.

In an embodiment, as shown in FIG. 6, in step S50, the first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian operator, which specifically includes the following steps:

S51: Obtain the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set, and use the Laplacian to sharpen the gray value of each pixel to obtain the sharpened pixels. grayscale value.

Specifically, the first enhanced iris image in the first enhanced iris image set can be directly read to obtain the gray value of each iris image pixel. The specific reading method is similar to step S21, and is not repeated here.

The Laplace operator based on second-order differential is defined as:

For the first enhanced iris image R (x, y), its second derivative is:

Therefore, Laplacian

for:

Get Laplace operator

After that, use Laplacian

Each pixel gray value of the gray value R (x, y) of the first enhanced iris image is sharpened according to the following formula to obtain the sharpened pixel gray value. ) Is the gray value of the sharpened pixel.

S52: Obtain a corresponding second enhanced iris image based on the sharpened pixel gray value in the first enhanced iris image.

The sharpened pixel gray value is replaced with the gray value at the original (x, y) pixel to obtain a second enhanced iris image.

In a specific embodiment, the Laplace operator

Four-neighbor sharpening template matrix

Laplace operator sharpening is performed on a first enhanced iris image in the first enhanced iris image set using a four-neighbor sharpening template matrix H.

As shown in Fig. 7 (a) and Fig. 7 (b), it shows the iris image after the initial contrast enhancement of the initial iris image (Fig. 7 (a)) and the sharpening of the Laplacian operator. Comparison of two enhanced iris images (Figure 7 (b)). It can be seen that the overall contrast of the initial iris image is low, and the overall contrast of the second enhanced iris image is effectively improved compared to the original iris image (the iris image in the human eye image shows more information), the dark pixels are brightened, and the edges The details are rich.

In this embodiment, first, the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set is obtained, and Laplacian sharpening processing is performed to obtain the sharpened pixel gray value. A corresponding second enhanced iris image is obtained. The iris image that has been enhanced by the optimized contrast algorithm is sharpened using Laplacian. The edge features of the image are enhanced while suppressing the highlights in the local enhancement process of the first enhanced iris image, thereby protecting the first enhancement. Details of the iris image. In addition, the above steps are not only simple and convenient, and improve the real-time performance of the iris image processing, but also the edge features of the second enhanced iris image are more prominent after processing, the overall contrast of the iris image set is greatly improved, and the texture characteristics of the iris image are enhanced. , It is helpful to improve the accuracy of iris image recognition.

It is worth noting that in order to verify the effectiveness of the local enhancement method of the iris image, the iris images of 50 human eyes were collected according to the method of steps S11 and S12 in this embodiment, and each human eye has a total of 600 iris image sets to calculate the iris. For the contrast of the image set, the top 3 contrasts of each human eye are selected, of which 2 are used for training and 1 is used for verification. After the 300 iris images are partially enhanced by the optimized contrast algorithm in this embodiment, the enhanced iris images are sharpened by using the method of steps S51 to S52 in this embodiment to obtain a processed training set and Validation set. Extract the texture features from the unprocessed training set and the processed training set respectively. The recognition algorithm recognizes them by calculating the Euclidean distance or by a Support Vector Machine (SVM) classifier, and calculates the comparison recognition rate as part of the iris image. Enhancement of the enhancement algorithm. The results show that the recognition rate of the unprocessed iris image is 83%, and the recognition rate of the iris image after the local enhancement method of the iris image in this embodiment is 98.9%, and the recognition rate is increased by 15.9%.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

In one embodiment, an iris image local enhancement device is provided. The iris image local enhancement device corresponds to the iris image local enhancement method in the above-mentioned one-to-one correspondence. As shown in FIG. 8, the iris image local enhancement device includes an iris image set acquisition module 10, an iris sequence acquisition module 20, an initial iris set acquisition module 30, a first enhanced iris image set acquisition module 40, and a second enhanced iris image set acquisition. Module 50. The implementation functions of the iris image set acquisition module 10, the iris sequence acquisition module 20, the initial iris set acquisition module 30, the first enhanced iris image set acquisition module 40, and the second enhanced iris image set acquisition module 50 are the same as those of the iris in the above embodiment. The steps corresponding to the image local enhancement method correspond one by one. In order to avoid redundant description, this embodiment is not detailed one by one.

The iris image set acquisition module 10 is configured to acquire an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier.

The iris sequence acquisition module 20 is used to calculate the contrast of the iris images in the iris image set, and sort the iris images corresponding to each user ID in the iris image set in the order of the contrast, to obtain the initial corresponding to each user ID. Iris sequence.

The initial iris set acquisition module 30 is configured to obtain a preset number of iris images from the initial iris sequence corresponding to each user identifier according to the order of increasing contrast, to form an initial iris set.

A first enhanced iris image set acquisition module 40 is configured to perform local enhancement processing on an initial iris image in an initial iris set using an optimized contrast algorithm to obtain a first enhanced iris image set.

A second enhanced iris image set acquisition module 50 is configured to sharpen the first enhanced iris image in the first enhanced iris image set using a Laplacian operator to obtain a second enhanced iris image set.

Specifically, the iris image set acquisition module 10 includes a measured distance detection unit 11 and an iris image set acquisition unit 12.

The measured distance detection unit 11 is configured to obtain the measured distance of the human eye and the camera in real time, and if the measured distance is not within the distance threshold, a prompt message is sent.

The iris image set obtaining unit 12 is configured to control the camera to continuously shoot if the actual measured distance is within a distance threshold, to obtain an iris image set.

Specifically, the iris sequence acquisition module 20 further includes a contrast calculation unit 21 for calculating the contrast of the iris image in the iris image set.

Specifically, the contrast calculation unit 21 includes a gray value acquisition sub-unit 211, a gray value difference acquisition sub unit 212, a gray number difference number acquisition sub unit 213, and a contrast calculation sub unit 214.

The gray value acquisition subunit 211 is configured to acquire a gray value of each pixel of the iris image in the iris image set, and sequentially use each pixel as a central pixel.

The gray value difference obtaining subunit 212 is configured to calculate a difference between a gray value of each center pixel and a gray value of a corresponding neighbor pixel according to a preset neighborhood size.

The number-of-gray-values acquisition subunit 213 is configured to obtain the number of differences between the gray-scale values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image.

The contrast calculation subunit 214 is configured to perform a square sum of the difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighboring pixel, and divide it by the number of differences Number to obtain the contrast of the iris image.

Specifically, the first enhanced iris image set acquisition module 40 further includes an atmospheric scattering model parameter acquisition unit 41 and a first enhanced iris image set acquisition unit 42.

The atmospheric scattering model parameter obtaining unit 41 is configured to calculate the atmospheric light region A and the transmittance t (x, y) of the initial iris image in the initial iris set based on the atmospheric scattering model.

The first enhanced iris image set acquisition unit 42 is configured to perform image restoration on the initial iris image based on the atmospheric light area A and the transmittance t (x, y):

Among them, (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the output of the optimized contrast algorithm The first enhanced gray value of the iris image.

Specifically, the second enhanced iris image set acquisition module 50 includes a sharpened gray value acquisition unit 51 and a second enhanced iris image acquisition unit 52.

The sharpened gray value obtaining unit 51 is configured to obtain the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set, and use the Laplacian to determine the gray value of each pixel. Perform sharpening to obtain the gray value of the sharpened pixel.

The second enhanced iris image acquisition unit 52 is configured to acquire a corresponding second enhanced iris image based on the sharpened pixel gray value in the first enhanced iris image.

For the specific limitation of the iris image local enhancement device, refer to the foregoing limitation on the iris image local enhancement method, which will not be repeated here. Each module in the above-mentioned iris image local enhancement device may be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 9. The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer-readable instructions. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in a non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer-readable instructions are executed by a processor to implement a method for local enhancement of an iris image.

In one embodiment, a computer device is provided, which includes a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. The processor implements the computer-readable instructions to implement the iris image of the foregoing embodiment. The steps of the local enhancement method include, for example, steps S10 to S50 shown in FIG. 2. Alternatively, when the processor executes the computer-readable instructions, the functions of the modules / units of the iris image local enhancement device of the embodiment described above are implemented, for example, modules 10 to 50 shown in FIG. 8. To avoid repetition, we will not repeat them here.

One or more non-volatile readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the iris image part in the above embodiment The steps of the enhancement method, or the functions of each module / unit of the iris image local enhancement device in the above embodiment are implemented when the computer-readable instructions are executed by one or more processors. To avoid repetition, details are not repeated here.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by using computer-readable instructions to instruct related hardware. The computer-readable instructions can be stored in a non-volatile computer. In the readable storage medium, the computer-readable instructions, when executed, may include the processes of the embodiments of the methods described above. Wherein, any reference to the storage, storage, database, or other media used in the embodiments provided in this application may include non-volatile storage. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.

Those skilled in the art can clearly understand that, for the convenience and brevity of the description, only the above-mentioned division of functional units and modules is used as an example. In practical applications, the above functions can be assigned by different functional units, Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to describe the technical solution of the present application, but not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still implement the foregoing implementations. The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of this application.

Claims (20)

1. A method for local enhancement of an iris image, comprising:

   Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

   Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

   Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

   Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

   The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set.
2. The method for locally enhancing an iris image according to claim 1, wherein the acquiring an iris image set comprises:

   Obtain the measured distance between the human eye and the camera in real time, and send a prompt message if the measured distance is not within the distance threshold;

   If the measured distance is within a distance threshold, the camera is controlled to perform continuous shooting to obtain the iris image set.
3. The method for locally enhancing an iris image according to claim 1, wherein the calculating the contrast of the iris image in the iris image set comprises:

   Acquiring the gray value of each pixel of the iris image in the iris image set, and sequentially using each pixel as the center pixel;

   Calculate the difference between the gray value of each center pixel and the gray value of the corresponding neighborhood pixel according to the preset neighborhood size;

   Obtaining the number of differences between the gray values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image;

   The difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighborhood pixel is squared and summed, and then divided by the number of the difference in the gray value in the iris image to obtain the iris image Contrast.
4. The method for local enhancement of an iris image according to claim 1, wherein the step of locally enhancing the initial iris image in the initial iris set by using an optimized contrast algorithm comprises:

   Calculating the atmospheric light area A and the transmittance t (x, y) of the initial iris image in the initial iris set based on the atmospheric scattering model;

   Based on the atmospheric light area A and the transmittance t (x, y), image restoration is performed on the initial iris image using the following formula:

   

   Where (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the optimized contrast The gray value of the first enhanced iris image output by the algorithm.
5. The method for locally enhancing an iris image according to claim 1, wherein the first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian operator, comprising:

   Obtain the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set, and use the Laplacian to sharpen the gray value of each pixel to obtain a sharpened pixel grayscale value;

   A corresponding second enhanced iris image is acquired based on the sharpened pixel gray value in the first enhanced iris image.
6. An iris image local enhancement device includes:

   An iris image set acquisition module, configured to obtain an iris image set, wherein the iris image set includes an iris image, and the iris image includes a user identifier;

   The iris sequence acquisition module is used to calculate the contrast of the iris images in the iris image set, and sort the iris images corresponding to each user ID in the iris image set in the order of increasing contrast, to obtain each user ID Corresponding initial iris sequence;

   The initial iris set acquisition module is used to obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

   A first enhanced iris image set acquisition module, configured to perform local enhancement processing on the initial iris image in the initial iris set using an optimized contrast algorithm;

   A second enhanced iris image set acquisition module is configured to sharpen the first enhanced iris image in the first enhanced iris image set by using a Laplacian to obtain a second enhanced iris image set.
7. The local iris image enhancement device according to claim 6, wherein the first enhanced iris image set acquisition module comprises:

   An atmospheric scattering model parameter obtaining unit, configured to calculate an atmospheric light region A and a transmittance t (x, y) of an initial iris image in the initial iris set based on the atmospheric scattering model;

   A first enhanced iris image set obtaining unit is configured to perform image restoration on the initial iris image based on the atmospheric light area A and the transmittance t (x, y):

   

   Where (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the optimized contrast The gray value of the first enhanced iris image output by the algorithm.
8. The local iris image enhancement device according to claim 6, wherein the second enhanced iris image set acquisition module comprises:

   A sharpened gray value obtaining unit is configured to obtain a gray value of each pixel of a first enhanced iris image in the first enhanced iris image set, and use a Laplacian operator for each pixel's gray value Both are sharpened to obtain the gray value of the sharpened pixel;

   A second enhanced iris image acquisition unit is configured to acquire a corresponding second enhanced iris image based on the sharpened pixel gray value in the first enhanced iris image.
9. The local iris image enhancement device according to claim 6, wherein the iris image set acquisition module comprises:

   A measured distance detection unit, configured to obtain the measured distance of the human eye and the camera in real time, and send a prompt message if the measured distance is not within the distance threshold;

   An iris image set acquiring unit is configured to control the camera to continuously shoot if the measured distance is within a distance threshold, to acquire the iris image set.
10. The local iris image enhancement device according to claim 6, wherein the iris sequence acquisition module comprises:

    A gray value acquisition subunit, configured to acquire a gray value of each pixel of an iris image in the iris image set, and sequentially use each pixel as a center pixel;

    A subunit for obtaining a difference in gray values, for calculating a difference between a gray value of each center pixel and a gray value of a corresponding neighbor pixel according to a preset neighborhood size;

    A subunit for obtaining the number of differences in gray values, for obtaining the number of differences in the gray values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image ;

    A contrast calculation subunit, configured to perform a square sum of the difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighboring pixel, and divide it by the difference between the gray values in the iris image Number, to obtain the contrast of the iris image.
11. A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. The computer-readable instructions are characterized in that the processor executes the computer-readable instructions. When computer-readable instructions are instructed, the following steps are implemented:

    Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

    Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

    Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

    Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

    The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set.
12. The computer device according to claim 11, wherein the acquiring an iris image set comprises:

    Obtain the measured distance between the human eye and the camera in real time, and send a prompt message if the measured distance is not within the distance threshold;

    If the measured distance is within a distance threshold, the camera is controlled to perform continuous shooting to obtain the iris image set.
13. The computer device of claim 11, wherein the calculating the contrast of an iris image in the iris image set comprises:

    Acquiring the gray value of each pixel of the iris image in the iris image set, and sequentially using each pixel as the center pixel;

    Calculate the difference between the gray value of each center pixel and the gray value of the corresponding neighborhood pixel according to the preset neighborhood size;

    Obtaining the number of differences between the gray values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image;

    The difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighborhood pixel is squared and summed, and then divided by the number of the difference in the gray value in the iris image to obtain the iris image Contrast.
14. The computer device according to claim 11, wherein the step of locally enhancing the initial iris image in the initial iris set by using an optimized contrast algorithm comprises:

    Calculating the atmospheric light area A and the transmittance t (x, y) of the initial iris image in the initial iris set based on the atmospheric scattering model;

    Based on the atmospheric light area A and the transmittance t (x, y), image restoration is performed on the initial iris image using the following formula:

    

    Where (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the optimized contrast The gray value of the first enhanced iris image output by the algorithm.
15. The computer device according to claim 11, wherein the first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian operator, comprising:

    Obtain the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set, and use the Laplacian to sharpen the gray value of each pixel to obtain a sharpened pixel grayscale value;

    A corresponding second enhanced iris image is acquired based on the sharpened pixel gray value in the first enhanced iris image.
16. One or more non-volatile readable storage media storing computer readable instructions, characterized in that when the computer readable instructions are executed by one or more processors, the one or more processors are caused to execute The following steps:

    Acquiring an iris image set, where the iris image set includes an iris image, and the iris image includes a user identifier;

    Calculating the contrast of the iris images in the iris image set, and sorting the iris images corresponding to each user ID in the iris image set in order of increasing contrast, to obtain an initial iris sequence corresponding to each user ID;

    Obtain a preset number of iris images from the initial iris sequence corresponding to each user ID in order of increasing contrast, to form an initial iris set;

    Using an optimized contrast algorithm to locally enhance the initial iris image in the initial iris set to obtain a first enhanced iris image set;

    The first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian to obtain a second enhanced iris image set.
17. The non-volatile readable storage medium according to claim 16, wherein the obtaining an iris image set comprises:

    Obtain the measured distance between the human eye and the camera in real time, and send a prompt message if the measured distance is not within the distance threshold;

    If the measured distance is within a distance threshold, the camera is controlled to perform continuous shooting to obtain the iris image set.
18. The non-volatile readable storage medium of claim 16, wherein the calculating the contrast of an iris image in the iris image set comprises:

    Acquiring the gray value of each pixel of the iris image in the iris image set, and sequentially using each pixel as the central pixel;

    Calculate the difference between the gray value of each center pixel and the gray value of the corresponding neighborhood pixel according to the preset neighborhood size;

    Obtaining the number of differences between the gray values in the iris image based on the preset neighborhood size and the number of rows and columns of the corresponding matrix of the iris image;

    The difference between the gray value of each central pixel in the iris image and the gray value of the corresponding neighborhood pixel is squared and summed, and then divided by the number of the difference in the gray value in the iris image to obtain the iris image Contrast.
19. The non-volatile readable storage medium according to claim 16, wherein performing the local enhancement processing on the initial iris image in the initial iris set by using an optimized contrast algorithm comprises:

    Calculating the atmospheric light area A and the transmittance t (x, y) of the initial iris image in the initial iris set based on the atmospheric scattering model;

    Based on the atmospheric light area A and the transmittance t (x, y), image restoration is performed on the initial iris image using the following formula:

    

    Where (x, y) is the coordinate value of the pixel in the initial iris image, I (x, y) is the gray value of the initial iris image input by the optimized contrast algorithm, and J (x, y) is the optimized contrast The gray value of the first enhanced iris image output by the algorithm.
20. The non-volatile readable storage medium according to claim 16, wherein the first enhanced iris image in the first enhanced iris image set is sharpened by using a Laplacian operator, comprising: :

    Obtain the gray value of each pixel of the first enhanced iris image in the first enhanced iris image set, and use the Laplacian to sharpen the gray value of each pixel to obtain a sharpened pixel grayscale value;

    A corresponding second enhanced iris image is acquired based on the sharpened pixel gray value in the first enhanced iris image.

Images