WO2019223066A1 - Procédé, dispositif et équipement d'amélioration globale pour image d'iris, et support d'informations - Google Patents

Procédé, dispositif et équipement d'amélioration globale pour image d'iris, et support d'informations Download PDF

Info

Publication number
WO2019223066A1
WO2019223066A1 PCT/CN2018/094394 CN2018094394W WO2019223066A1 WO 2019223066 A1 WO2019223066 A1 WO 2019223066A1 CN 2018094394 W CN2018094394 W CN 2018094394W WO 2019223066 A1 WO2019223066 A1 WO 2019223066A1
Authority
WO
WIPO (PCT)
Prior art keywords
iris image
iris
enhanced
pixel
gray value
Prior art date
Application number
PCT/CN2018/094394
Other languages
English (en)
Chinese (zh)
Inventor
李占川
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2019223066A1 publication Critical patent/WO2019223066A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G06V40/193—Preprocessing; Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G06V40/197—Matching; Classification

Definitions

  • 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 globally enhancing an iris image.
  • iris has the characteristics of uniqueness, stability, collectability and non-invasiveness.
  • 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.
  • the overall contrast of the collected iris image is usually adjusted dynamically.
  • the accuracy of the iris image processed in the recognition system is still not high.
  • a global iris image enhancement method includes:
  • the iris image set includes an iris image, and the iris image includes a user identifier
  • 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 global enhancement device includes:
  • An iris image set acquisition module configured to acquire an iris image set, where the iris image set includes an iris image, and the iris image includes a user logo;
  • 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 a global enhancement process on the initial iris image in the initial iris set using a logarithmic transformation 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.
  • the processor executes the computer-readable instructions, the following steps are implemented:
  • the iris image set includes an iris image, and the iris image includes a user identifier
  • 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:
  • the iris image set includes an iris image, and the iris image includes a user identifier
  • 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.
  • FIG. 1 is an application scenario diagram of a global iris image enhancement method according to an embodiment of the present application
  • FIG. 2 is a flowchart of a method for globally enhancing an iris image in an embodiment of the present application
  • step S10 in FIG. 2 is a flowchart of a specific implementation of step S10 in FIG. 2;
  • step S20 in FIG. 2 is a flowchart of a specific implementation of step S20 in FIG. 2;
  • step S50 in FIG. 2 is a flowchart of a specific implementation of step S50 in FIG. 2;
  • FIG. 6 (a) is an example diagram of an initial iris image in the embodiment of the present application.
  • FIG. 6 (b) is an example diagram of a second enhanced iris image in the embodiment of the present application.
  • FIG. 7 is a schematic diagram of an iris image global enhancement device according to an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a computer device in an embodiment of the present application.
  • the global iris image enhancement method provided in this application can be applied in a computer device or system to enhance the iris image to solve the problem that the iris image recognition accuracy is not high.
  • the computer device may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the system may include a server and a client.
  • FIG. 1 shows an application scenario diagram of the global enhancement method of the iris image applied in the system.
  • 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.
  • the server can be implemented by a server or a server cluster composed of a plurality of servers.
  • a method for globally enhancing an iris image is provided.
  • the method is applied to a computer device as an example for description, and includes the following steps:
  • 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
  • the iris image refers to an image obtained by photographing an iris inside a user's eye through a camera device.
  • the iris image set may be acquired in real time, or may be stored in a computer device in advance.
  • An iris image set can include one user's iris image, or it can include multiple user's iris images.
  • 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.
  • 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.
  • 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.
  • 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 different iris images in the acquired iris image set 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.
  • contrast is an index to measure image quality.
  • 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.
  • 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.
  • contrast calculation is performed on multiple iris images of each user ID one by one, and based on the same user ID, the multiple iris images corresponding to each user ID are sorted according to the order of the contrast from large to small to obtain each The sub-iris sequence corresponding to the user ID.
  • 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.
  • each user ID includes M iris images
  • 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.
  • contrast is an important indicator of image quality
  • the larger iris image is used as the selection criterion of the iris image, which is convenient for subsequent selection of iris images with larger contrast for processing to obtain iris images 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.
  • 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.
  • 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 iris 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.
  • 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 Use a logarithmic transformation algorithm to perform global enhancement processing on the initial iris image in the initial iris set to obtain a first enhanced iris image set.
  • the logarithmic transformation algorithm is a kind of image grayscale transformation algorithm, which refers to an algorithm that has a logarithmic relationship between the grayscale values of the pixels of the output image and the corresponding input image.
  • the logarithmic transformation algorithm is used to change the range of the gray value of the image by using the principle of mapping change to enhance the image.
  • Global enhancement refers to the enhancement of the entire image, that is, the entire image is directly enhanced. Unlike local enhancement, global enhancement does not need to divide the image into sub-blocks, which reduces the enhancement processing of sub-blocks, thereby improving the algorithm. effectiveness.
  • the logarithmic transformation algorithm conforms to the visual characteristics of the human eye.
  • the use of the logarithmic transformation algorithm can effectively enhance the contrast of the image, help to highlight the texture information of the image, and improve the visual effect of the image.
  • the logarithmic transformation algorithm can be used to expand the low gray value portion of the initial iris image, showing more details of the low gray value portion, compressing the high gray value portion of the initial iris image, and reducing the details of the high gray value portion , So as to achieve the purpose of emphasizing the low gray value part of the image.
  • the logarithmic transformation algorithm is used to globally enhance the initial iris image, so that the gray value of the dark area pixels of the initial iris image is greatly improved, so that the dark area detail information of the initial iris image is enhanced, and the light area pixels are enhanced.
  • the gray value is compressed, which effectively scales the dynamic range of the gray value of the pixel in the initial iris image, making the dark area details more prominent. Therefore, the logarithmic transformation algorithm is used to globally enhance the initial iris image in the initial iris set, so that the detailed information of the dark area of the initial iris image is enhanced, so that the first enhanced iris image set with rich and clear texture can be obtained, and subsequent Recognition accuracy.
  • the gray value of the initial iris image in the initial iris set is the input of the logarithmic transformation algorithm
  • the gray value of the first enhanced iris image is the output of the logarithmic transformation algorithm.
  • 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 performing a logarithmic transformation algorithm on the initial iris image in the initial iris set.
  • Laplacian operator is a second-order differential operator, which is suitable for improving the 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, and the degree of blurring is relative to the image 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 Laplace operator, the image edge details are enhanced, and the sharpness of the bright area edge of the first enhanced iris image is also improved, thereby protecting the first enhanced iris image. detail.
  • 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, in order to obtain an iris training set with richer and clearer texture details and improve the recognition effect.
  • the logarithmic transformation algorithm enhances the details of the dark area of the original iris image, and at the same time, the difference between the gray values of the pixels in the bright area becomes smaller.
  • the Laplace operator is used to sharpen the gray value of the pixels of the first iris image to increase the sharpness of the bright area edges. Therefore, using the Laplacian operator to sharpen the first enhanced iris image can overcome the compression of areas with similar gray values of pixels in the bright area in the logarithmic transformation algorithm, so that the sharpness of the edges of the bright area is enhanced.
  • 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.
  • the enhanced first enhanced iris image Perform sharpening processing to improve the sharpness of the edges of the bright area of the first enhanced iris image, and obtain a second enhanced iris image after the sharpening processing, retaining more details of the iris image, and improving the contrast of the image as a whole, so that The texture features of the iris image are more clear, so as to obtain an iris training set with richer and clearer texture details, and improve the accuracy of subsequent recognition.
  • 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.
  • the measured distance refers to the distance between the user's eyes and the camera
  • 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”).
  • the measured distance 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.
  • the focal length of the infrared camera can be used as the distance threshold.
  • the infrared camera can be manually or automatically adjusted to determine the focal length of the camera according to the sharpness of the image. .
  • 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.
  • 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.
  • 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
  • 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.
  • step S20 the contrast of the iris image in the iris image set is calculated, and specifically includes the following steps:
  • 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.
  • 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.
  • 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:
  • jpg is the format of the image
  • lean is the name of the image
  • D: ⁇ is the path of the lean image
  • 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.
  • the boundary pixel 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.
  • the matrix of an iris image is:
  • 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.
  • 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.
  • the neighborhood pixel refers to the pixel adjacent to the center pixel position.
  • 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).
  • 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).
  • 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:
  • the number k of differences between gray values can be obtained by the following formula:
  • the number k of differences between gray values can be obtained by the following formula:
  • 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:
  • the contrast C is a specific value.
  • the gray value of each pixel of the iris image is obtained, and each pixel is sequentially used as the center pixel.
  • 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.
  • 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.
  • step S40 the logarithmic transformation algorithm is used to perform global enhancement processing on the initial iris image in the initial iris set, which specifically includes:
  • (x, y) is the coordinate value of the pixel in the initial iris image
  • f (x, y) is the gray value of the initial iris image input by the logarithmic transformation algorithm
  • L (x, y) is the logarithmic transformation
  • the gray value of the first enhanced iris image output by the algorithm, the position parameter k is used to adjust the position of the logarithmic transformation algorithm curve, and the shape parameters p and q are used to adjust the shape of the logarithmic transformation algorithm curve.
  • the gray value f (x, y) of the initial iris image is acquired by an image information acquisition tool.
  • the initial value of each parameter is estimated based on the information of the initial iris image, the value range of each parameter is determined, and the parameter k, p, and q parameter values are finally determined to achieve the best image enhancement effect.
  • the position parameter k determines the position of the logarithmic transformation algorithm curve
  • the shape parameters p and q determine the shape of the logarithmic transformation algorithm curve.
  • the logarithmic transformation algorithm processes the grayscale values of the dark area pixels and the grayscale values of the light area pixels in the initial iris image differently.
  • the grayscale values of the dark area pixels are stretched so that the dark area details
  • the information is enhanced, and the gray values of the pixels in the bright area are compressed, so that the dynamic range of the gray value of the initial iris image is effectively stretched. Therefore, the detailed information of the first enhanced iris image is effectively enhanced.
  • 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.
  • 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:
  • 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.
  • 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.
  • 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.
  • FIG. 6 (a) and Fig. 6 (b) it shows the iris image enhanced by a logarithmic transformation algorithm and sharpened by the Laplacian operator on an initial iris image (Fig. 6 (a)). Comparative view of the second enhanced iris image ( Figure 6 (b)). It can be seen that the overall contrast of the initial iris image is low. Compared with the original iris image, the gray value of the pixels in the dark area is effectively improved (the iris image in the human eye image becomes brighter), and the dark area details information It is more prominent and the edges are more detailed.
  • 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 logarithmic transformation algorithm is sharpened using Laplacian.
  • the image edge details are enhanced while the sharpness of the edges of the bright area of the first enhanced iris image is improved, thereby protecting the first An enhanced detail of the iris image.
  • 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.
  • the iris images of 50 human eyes were collected according to the method of step S11 and step S12 in this embodiment, and each human eye has a total of 600 iris image sets to calculate the iris
  • the top 3 contrasts of each human eye are selected, of which 2 are used for training and 1 is used for verification.
  • 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. The unprocessed training set and the processed training set are used to extract the texture features respectively.
  • the recognition algorithm is implemented by calculating the Euclidean distance or by a Support Vector Machine (SVM) classifier.
  • SVM Support Vector Machine
  • the comparison recognition rate is calculated and used as the global enhancement of the iris image. Enhancement of the algorithm.
  • the results show that the recognition rate of the unprocessed iris image is 83%, the recognition rate of the iris image after the global enhancement method of the iris image in this embodiment is 99.1%, and the recognition rate is increased by 16.1%.
  • an iris image global enhancement device corresponds to the iris image global enhancement method in the embodiment described above.
  • the iris image global 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 global image 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 a global enhancement process on the initial iris image in the initial iris set using a logarithmic transformation 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.
  • 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.
  • 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.
  • 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.
  • the first enhanced iris image set acquisition module 40 is configured to perform global enhancement processing on the initial iris image in the initial iris set using the following formula:
  • (x, y) is the coordinate value of the pixel in the initial iris image
  • f (x, y) is the gray value of the initial iris image input by the logarithmic transformation algorithm
  • L (x, y) is the logarithmic transformation
  • the position parameter k is used to adjust the position of the curve in the logarithmic transformation algorithm
  • the shape parameters p and q are used to adjust the shape of the curve in the logarithmic transformation algorithm.
  • 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.
  • Each module in the above-mentioned iris image global 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.
  • a computer device is provided.
  • the computer device may be a client, and its internal structure diagram may be as shown in FIG. 8.
  • 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 global enhancement of an iris image.
  • a computer device 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 global enhancement method are, for example, steps S10 to S50 shown in FIG. 2.
  • the functions of the modules / units of the iris image global enhancement device of the foregoing embodiment are implemented, for example, modules 10 to 50 shown in FIG. 7. To avoid repetition, we will not repeat them here.
  • One or more non-volatile readable storage media storing computer-readable instructions, when the computer-readable instructions are executed by one or more processors, cause the one or more processors to perform the global enhancement of the iris image of the embodiment described above
  • the steps of the method, or the functions of each module / unit of the iris image global enhancement device of the embodiment described above are implemented when the computer-readable instructions are executed by one or more processors. To avoid repetition, details are not repeated here.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Ophthalmology & Optometry (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Image Processing (AREA)

Abstract

La présente invention concerne un procédé, un dispositif et un équipement d'amélioration globale pour une image d'iris, et un support d'informations. Le procédé d'amélioration globale pour une image d'iris consiste à : acquérir un ensemble d'images d'iris ; calculer les rapports de contraste des images d'iris, et trier les images d'iris correspondant à chaque identifiant selon une séquence de rapports de contraste décroissant de façon à obtenir une séquence d'iris initiale ; obtenir un nombre prédéfini d'images d'iris à partir de la séquence d'iris initiale correspondant à chaque identifiant selon la séquence de rapports de contraste décroissant, de manière à former un ensemble d'iris initial ; réaliser une amélioration globale sur les images d'iris initiales à l'aide d'un algorithme de transformation logarithmique pour obtenir un premier ensemble d'images d'iris améliorées ; et améliorer la netteté des premières images d'iris améliorées à l'aide d'un opérateur de Laplace de sorte à obtenir un second ensemble d'images d'iris améliorées. Selon le présent procédé d'amélioration globale pour des images d'iris, non seulement les informations de détail de zones sombres des images d'iris sont améliorées, mais la netteté des bords de zones lumineuses des images d'iris est améliorée, l'effet d'amélioration est bon, et la précision de reconnaissance des images d'iris est améliorée.
PCT/CN2018/094394 2018-05-25 2018-07-04 Procédé, dispositif et équipement d'amélioration globale pour image d'iris, et support d'informations WO2019223066A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810511977.8A CN108830174A (zh) 2018-05-25 2018-05-25 虹膜图像全局增强方法、装置、设备及存储介质
CN201810511977.8 2018-05-25

Publications (1)

Publication Number Publication Date
WO2019223066A1 true WO2019223066A1 (fr) 2019-11-28

Family

ID=64145359

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/094394 WO2019223066A1 (fr) 2018-05-25 2018-07-04 Procédé, dispositif et équipement d'amélioration globale pour image d'iris, et support d'informations

Country Status (2)

Country Link
CN (1) CN108830174A (fr)
WO (1) WO2019223066A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112652010A (zh) * 2020-12-25 2021-04-13 安徽工业大学 一种基于图像增强和霍夫变换的球团粒径检测方法
CN117036205B (zh) * 2023-10-10 2023-12-26 深圳市悦和精密模具有限公司 基于图像增强的注塑件生产质量检测方法
CN117059047B (zh) * 2023-10-12 2023-12-22 深圳市柯达科电子科技有限公司 一种用于lcd显示图像色彩智能调整方法

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070047833A1 (en) * 2005-08-25 2007-03-01 Yan Zhang System or method for enhancing an image
CN101231747A (zh) * 2007-01-22 2008-07-30 清华大学 一种x射线图像边缘和对比度综合增强方法
US20080205785A1 (en) * 2005-11-23 2008-08-28 Cedara Software Corp. Method and system for enhancing digital images
CN103500442A (zh) * 2013-09-29 2014-01-08 华南理工大学 集成电路封装中的x射线图像多尺度细节增强方法
CN106339994A (zh) * 2016-08-29 2017-01-18 合肥康胜达智能科技有限公司 一种图像增强方法
CN106548113A (zh) * 2015-09-16 2017-03-29 上海市公安局刑事侦查总队 图像识别方法及系统
CN106570836A (zh) * 2016-11-03 2017-04-19 北京航星机器制造有限公司 一种辐射图像局部增强方法
CN107945163A (zh) * 2017-11-23 2018-04-20 广州酷狗计算机科技有限公司 图像增强方法及装置

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070047833A1 (en) * 2005-08-25 2007-03-01 Yan Zhang System or method for enhancing an image
US20080205785A1 (en) * 2005-11-23 2008-08-28 Cedara Software Corp. Method and system for enhancing digital images
CN101231747A (zh) * 2007-01-22 2008-07-30 清华大学 一种x射线图像边缘和对比度综合增强方法
CN103500442A (zh) * 2013-09-29 2014-01-08 华南理工大学 集成电路封装中的x射线图像多尺度细节增强方法
CN106548113A (zh) * 2015-09-16 2017-03-29 上海市公安局刑事侦查总队 图像识别方法及系统
CN106339994A (zh) * 2016-08-29 2017-01-18 合肥康胜达智能科技有限公司 一种图像增强方法
CN106570836A (zh) * 2016-11-03 2017-04-19 北京航星机器制造有限公司 一种辐射图像局部增强方法
CN107945163A (zh) * 2017-11-23 2018-04-20 广州酷狗计算机科技有限公司 图像增强方法及装置

Also Published As

Publication number Publication date
CN108830174A (zh) 2018-11-16

Similar Documents

Publication Publication Date Title
WO2019223069A1 (fr) Procédé, appareil et dispositif d'amélioration d'images d'iris reposant sur un histogramme, et support d'informations
WO2019223068A1 (fr) Procédé d'amélioration locale d'images d'iris, dispositif, équipement et support d'informations
WO2019153739A1 (fr) Procédé, dispositif et appareil d'authentification d'identité basés sur la reconnaissance faciale, et support de stockage
JP7413400B2 (ja) 肌質測定方法、肌質等級分類方法、肌質測定装置、電子機器及び記憶媒体
WO2020000908A1 (fr) Procédé et dispositif de détection de vivacité de visage
JP6255486B2 (ja) 情報認識のための方法及びシステム
WO2022199583A1 (fr) Procédé et appareil de traitement d'image, dispositif informatique et support de stockage
CN106056064A (zh) 一种人脸识别方法及人脸识别装置
CN108090511B (zh) 图像分类方法、装置、电子设备及可读存储介质
CN108810406B (zh) 人像光效处理方法、装置、终端及计算机可读存储介质
WO2019223066A1 (fr) Procédé, dispositif et équipement d'amélioration globale pour image d'iris, et support d'informations
JP2021531571A (ja) 証明書画像抽出方法及び端末機器
CN111915541B (zh) 基于人工智能的图像增强处理方法、装置、设备及介质
CN111383232A (zh) 抠图方法、装置、终端设备及计算机可读存储介质
CN112381081A (zh) 公章文字自动识别方法、装置、计算机设备及存储介质
CN111935479A (zh) 一种目标图像确定方法、装置、计算机设备及存储介质
WO2021139447A1 (fr) Appareil et procédé de détection de cellule cervicale anormale
WO2017177616A1 (fr) Procédé et dispositif de reconnaissance de visage, et procédé et dispositif d'affichage d'image
WO2019223067A1 (fr) Procédé et appareil d'amélioration d'images d'iris reposant sur un traitement multiple, et dispositif et support
RU2697627C1 (ru) Способ корректировки освещенности объекта на изображении в последовательности изображений и вычислительное устройство пользователя, реализующее упомянутый способ
CN108289176B (zh) 一种拍照搜题方法、搜题装置及终端设备
CN110942067A (zh) 文本识别方法、装置、计算机设备和存储介质
CN112839167A (zh) 图像处理方法、装置、电子设备及计算机可读介质
CN117496019B (zh) 一种驱动静态图像的图像动画处理方法及系统
CN109447942B (zh) 图像模糊度确定方法、装置、计算机设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18920141

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 11/03/2021)

122 Ep: pct application non-entry in european phase

Ref document number: 18920141

Country of ref document: EP

Kind code of ref document: A1