Palm vein recognition system, method and device suitable for large throughput

Abstract

The embodiment of the invention discloses a palm vein recognition system, method and device suitable for large throughput, and the method comprises the steps: collecting palm vein image data through an image collector, recognizing the palm vein image data through an image detection module, obtaining a palm region, carrying out the alignment processing of the palm region, extracting the feature information of a palm vein vector, and carrying out the recognition of the palm vein vector. The server classifies the palm vein vector feature information to obtain a similar feature set, calculates weight distances between the palm vein vector feature information and other features in the similar feature set, and matches a target search result based on the weight distances; a set of similar features is searched through the pre-clustering technology, feature matching is helped to be accelerated, the search range is narrowed through the pre-search technology, the problem that real-time performance and accuracy are difficult to consider when a large-scale group needs to be authenticated is effectively solved, the method can be widely applied to the fields of payment, airports, rail transit, finance, banks and the like, the passing efficiency is remarkably improved, and the user experience is improved. And the system security is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of biometric identification technology, and in particular to a palm vein identification system, method and device suitable for large throughput. Background Art

[0002] At present, palm vein recognition has been gradually applied in biometric identification scenarios due to its characteristics of being difficult to forge, highly secure, easy to identify, good anti-interference performance, non-contact, and long palm vein feature change cycle. The existing palm vein recognition process generally consists of the following steps: palm photo acquisition, palm shape detection, feature extraction, feature matching, and returning matching results. However, the current palm vein recognition technology mostly focuses on scenarios with small recognition volume, such as writing access control and unlocking personal devices. The designs of terminal modules, protocol systems, and server distribution included in it cannot be applied to scenarios with large throughput and high concurrency, such as payment, airports, rail transit, finance, and banks. Summary of the invention

[0003] In view of the above-mentioned defects, the embodiments of the present invention disclose a palm vein recognition system, method and device suitable for high-throughput applications, which can quickly match palm vein features and are suitable for high-throughput applications.

[0004] A first aspect of an embodiment of the present invention discloses a palm vein recognition system suitable for large-volume processing, comprising: a server, an image collector and an image detection module, wherein the image collector collects palm vein image data, the image detection module is used to identify the palm vein image data to obtain a palm area, align the palm area, and extract palm vein vector feature information of the palm area and send it to the server; the server is used to classify the received palm vein vector feature information to obtain a similar feature set whose similarity with the palm vein vector information is greater than a set threshold, calculate the similarity distance between the palm vein vector feature information and other features in the similar feature set, and match the target search results based on the similarity distance.

[0005] A second aspect of an embodiment of the present invention discloses a palm vein recognition method applicable to large throughput, comprising:

[0006] Acquiring palm vein image data in response to an image acquisition instruction;

[0007] Processing the palm vein image data to obtain palm vein vector feature information;

[0008] The palm vein vector feature information is preprocessed to obtain a similar feature set matching the palm vein vector feature information, and the target search result is matched based on the similar feature set.

[0009] As an optional implementation, in the second aspect of the embodiment of the present invention, the processing of the palm vein image data to obtain palm vein vector feature information includes:

[0010] Recognize the palm vein image data to obtain a palm area;

[0011] The palm area is aligned and the palm vein vector feature information of the palm area is extracted.

[0012] As an optional implementation, in the second aspect of the embodiment of the present invention, the identifying the palm vein image data to obtain the palm area includes:

[0013] Binarizing the palm vein image data to obtain a palm size, and obtaining a palm area based on the palm size;

[0014] Accordingly, the alignment process for the palm area includes:

[0015] Performing regression detection on the palm area to detect the coordinates of target key points in the palm area;

[0016] The palm area is aligned based on the target key point coordinates.

[0017] As an optional implementation, in the second aspect of the embodiment of the present invention, preprocessing the palm vein vector feature information, obtaining a similar feature set matching the palm vein vector feature information, and matching the target search results based on the similar feature set include:

[0018] Classifying the palm vein vector feature information to obtain at least one similar feature set having a similarity with the palm vein vector feature information greater than a set threshold;

[0019] Calculate the weighted distance between the palm vein vector feature information and each similar feature set respectively;

[0020] The target search results are matched based on the weighted distance.

[0021] As an optional implementation, in the second aspect of the embodiment of the present invention, respectively calculating the weighted distance between the palm vein vector feature information and each similar feature set includes:

[0022] The first distance between the palm vein vector feature information x and the similar feature set is calculated according to Formula 1, and each feature node in the similar feature set is obtained; Formula 1 is: Among them, d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information.

[0023] The second distance between the palm vein vector feature information and each feature node is calculated respectively according to Formula 2; the Formula 2 is: Where d2 is the second distance;

[0024] The weighted distance is calculated based on Formula 3: d=N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M.

[0025] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the matching target search results based on the weighted distance includes:

[0026] Filtering a target feature set from at least one similar feature set according to a weighted distance;

[0027] According to Formula 2, the feature node with the second smallest distance in the target feature set is matched as the target search result.

[0028] A third aspect of an embodiment of the present invention discloses a palm vein recognition device suitable for large throughput, comprising:

[0029] Image acquisition module: used for acquiring palm vein image data in response to an image acquisition instruction;

[0030] Information processing module: used for processing the palm vein image data to obtain palm vein vector feature information;

[0031] Feature matching module: used to pre-process the palm vein vector feature information, obtain a similar feature set that matches the palm vein vector feature information, and match the target search results based on the similar feature set.

[0032] As an optional implementation manner, in a third aspect of the embodiment of the present invention, the information processing module includes:

[0033] Identification submodule: used for identifying the palm vein image data to obtain the palm area;

[0034] Extraction submodule: used for aligning the palm area and extracting palm vein vector feature information of the palm area.

[0035] As an optional implementation, in a third aspect of the embodiment of the present invention, the identifying the palm vein image data to obtain the palm area includes:

[0036] Binarizing the palm vein image data to obtain a palm size, and obtaining a palm area based on the palm size;

[0037] Accordingly, the alignment process for the palm area includes:

[0038] Performing regression detection on the palm area to detect the coordinates of target key points in the palm area;

[0039] The palm area is aligned based on the target key point coordinates.

[0040] As an optional implementation manner, in a third aspect of the embodiment of the present invention, the feature matching module includes:

[0041] Classification submodule: used for classifying the palm vein vector feature information to obtain at least one similar feature set having a similarity with the palm vein vector feature information greater than a set threshold;

[0042] Calculation submodule: used to calculate the weighted distance between the palm vein vector feature information and each similar feature set;

[0043] Matching submodule: used for matching target search results based on the weighted distance.

[0044] As an optional implementation, in the third aspect of the embodiment of the present invention, respectively calculating the weighted distance between the palm vein vector feature information and each similar feature set includes:

[0045] The first distance between the palm vein vector feature information x and the similar feature set is calculated according to Formula 1, and each feature node in the similar feature set is obtained; Formula 1 is: Among them, d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information.

[0046] The second distance between the palm vein vector feature information and each feature node is calculated respectively according to Formula 2; the Formula 2 is: Where d2 is the second distance;

[0047] The weighted distance is calculated based on Formula 3: d=N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M.

[0048] As an optional implementation manner, in the third aspect of the embodiment of the present invention, the matching target search result based on the weighted distance includes:

[0049] Filtering a target feature set from at least one similar feature set according to a weighted distance;

[0050] According to Formula 2, the feature node with the second smallest distance in the target feature set is matched as the target search result.

[0051] A fourth aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the palm vein recognition method suitable for large throughput disclosed in the second aspect of the embodiment of the present invention.

[0052] A fifth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the palm vein recognition method applicable to large throughput disclosed in the second aspect of an embodiment of the present invention.

[0053] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0054] In the embodiment of the present invention, palm vein image data is collected by an image collector, and the palm vein image data is identified by an image detection module to obtain a palm area, and palm vein vector feature information is extracted after alignment processing is performed on the palm area. The server classifies the palm vein vector feature information to obtain a similar feature set, calculates the weighted distance between the palm vein vector feature information and other features in the similar feature set, and matches the target search results based on the weighted distance; uses a pre-clustering technology to find a set of similar features to help accelerate feature matching, and uses a pre-search technology to narrow the search range, effectively solving the problem of difficulty in balancing real-time and accuracy when large-scale groups need authentication. The server can be widely used in payment, airports, rail transit, finance, banks and other fields, significantly improving travel efficiency and enhancing system security. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 It is a module schematic diagram of a palm vein recognition system suitable for large throughput disclosed in an embodiment of the present invention;

[0057] Figure 2 It is a flowchart of a palm vein recognition method applicable to large throughput disclosed in an embodiment of the present invention;

[0058] Figure 3 1 is a schematic diagram of the structure of a palm vein recognition device suitable for large throughput provided by an embodiment of the present invention;

[0059] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] The embodiments of the present invention disclose a palm vein recognition system, method, device, electronic device and storage medium suitable for large-volume palm vein recognition. In the embodiments, palm vein image data is collected by an image collector, and the palm vein image data is identified by an image detection module to obtain a palm area. Palm vein vector feature information is extracted after alignment processing is performed on the palm area. The server classifies the palm vein vector feature information to obtain a similar feature set, calculates the weight distance between the palm vein vector feature information and other features in the similar feature set, and matches the target search results based on the weight distance. A set of similar features is found through a pre-clustering technology to help accelerate feature matching, and a search range is narrowed through a pre-search technology, which effectively solves the problem of difficulty in balancing real-time and accuracy when large-scale groups need authentication. The system can be widely used in payment, airports, rail transit, finance, banks and other fields, significantly improving traffic efficiency and enhancing system security.

[0063] Embodiment 1

[0064] See also Figure 1 , Figure 1 Schematic diagram of the module structure of a palm vein recognition system suitable for large throughput disclosed in an embodiment of the present invention. Figure 1As shown, the system includes a server, an image collector and an image detection module, the image collector collects palm vein image data, the image detection module is used to identify the palm vein image data to obtain the palm area, align the palm area, and extract the palm vein vector feature information of the palm area and send it to the server; the server is used to classify the received palm vein vector feature information to obtain a similar feature set whose similarity with the palm vein vector information is greater than a set threshold, calculate the weighted distance between the palm vein vector feature information and other features in the similar feature set, and match the target search result based on the weighted distance.

[0065] The image detection module specifically includes, for example, a tensor processing unit TPU, which accelerates the processing of image data and outputs a palm detection frame to detect the palm area, and then uses a vector algorithm to align the palm area.

[0066] The image collector of the embodiment is a high-definition camera or other image sensor in the terminal, which ensures that the collected palm vein image is clear and rich in details, provides high-quality raw data for subsequent recognition processing, and usually supports fast and continuous acquisition to meet the needs of high-throughput scenarios and reduce user waiting time. The image detection module uses image processing techniques such as edge detection and color segmentation to identify the palm area in the image and eliminate interference. The identified palm area is standardized, and the reference point of the palm area is found for alignment to ensure that the palm vein images obtained under different acquisition conditions are consistent during feature extraction. A feature extraction algorithm such as a convolutional neural network of deep learning is used to extract unique and stable vector feature information from the palm vein image of the palm area.

[0067] Specifically, the palm vein image data is binarized to obtain the palm size, the palm area is obtained based on the palm size, regression detection is performed on the palm area, and the coordinates of the target key points in the palm area are detected; and the palm area is aligned based on the target key point coordinates.

[0068] Further, the processor calculates the first distance between the palm vein vector feature information x and the similar feature set according to Formula 1, and obtains each feature node in the similar feature set; Formula 1 is: Wherein, d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information. The second distance between the palm vein vector feature information and each feature node is calculated according to Formula 2; Formula 2 is: Where d2 is the second distance; the weighted distance is calculated based on Formula 3: d = N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M. Filter the target feature set from at least one similar feature set based on the weighted distance; match the feature node with the smallest second distance in the target feature set based on Formula 2 as the target search result.

[0069] Embodiment 2

[0070] See also Figure 2 , Figure 2 It is a flow chart of a palm vein recognition method suitable for high throughput disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 2 As shown, the palm vein recognition method suitable for large throughput includes the following steps:

[0071] 201. Acquire palm vein image data in response to an image acquisition instruction.

[0072] This step is usually triggered by a user operation (such as pressing a button, touching a screen, etc.), or by a preset condition in some automated systems, and the palm vein image is captured by a camera or an infrared camera.

[0073] 202. Process the palm vein image data to obtain palm vein vector feature information.

[0074] In this step, image processing may include image denoising, contrast enhancement, edge detection, etc. to improve the clarity and recognizability of the vein structure, and convert the processed image into a set of mathematical features that can represent the unique pattern of palm veins. These features are usually expressed in the form of vectors, called palm vein vector feature information. Feature extraction methods may include template matching, feature point detection, texture analysis, etc.

[0075] Specifically, the palm vein image data is identified to obtain a palm area; the palm area is aligned, and palm vein vector feature information of the palm area is extracted.

[0076] Identifying the palm area is to accurately separate the palm area from the original image and exclude the interference of the background and other non-palm parts. Color segmentation can be used to segment the palm using the difference in color between the palm and the background. For example, if the background is uniform, a simple threshold segmentation method can be used. Edge detection can also be used to identify the outline of the palm by detecting the edges in the image. Common edge detection algorithms include Sobel, Canny, etc., or shape analysis methods, which use the unique shape features of the palm (such as aspect ratio, roundness, etc.) to further confirm the palm area, and the palm area in the image can also be identified by training the model. In the alignment process of the embodiment, it is to ensure that the palm vein images obtained under different acquisition conditions (such as angle, distance, light, etc.) are consistent during feature extraction, thereby improving the recognition accuracy. For example, selection is made according to the direction of the palm so that the palm direction in all images is unified. In addition, the image size can be adjusted so that the palm area occupies a similar proportion in the image. In the embodiment, at least one reference point can be set in advance, and then the reference point is found for the palm area to achieve alignment.

[0077] The step of identifying the palm vein image data to obtain the palm area includes: performing binarization calculation on the palm vein image data to obtain the palm size, and obtaining the palm area based on the palm size. The step of aligning the palm area includes: performing regression detection on the palm area to detect the target key point coordinates in the palm area; and aligning the palm area based on the target key point coordinates.

[0078] First, the palm vein image is binarized, which is the process of converting the pixels in the image to 0 or 1. It is usually determined based on a threshold. In the palm vein image, due to the contrast difference between the veins and the surrounding tissues, the veins can be separated from the background by selecting an appropriate threshold. In order to obtain the palm size more accurately, morphological operations such as corrosion, expansion, opening and closing operations are usually performed after binarization, which can help remove small noise points, fill small holes, and smooth the image edges. The outer contour of the palm is found through contour detection algorithms (such as Canny edge detection and then finding contours). This contour can approximately represent the size and shape of the palm. Based on the detected contour, the size of the palm, such as width, height, etc., can be calculated. This size information will be used for subsequent palm area cropping or positioning. Using the calculated palm size, the palm area is cropped or located in the original image.

[0079] Afterwards, the embodiment can detect the coordinates of the target key points in the palm area through a regression algorithm (such as a regression tree in machine learning, a convolutional neural network in deep learning, etc.). These key points can be specific anatomical points of the palm (such as the wrist, the base of the fingers, etc.), or they can be manually defined alignment reference points (such as the center point of the palm, the intersection of the veins, etc.). The selection of key points should be based on their importance to palm alignment. For example, the position information of the wrist and the base of the fingers can help determine the direction and size of the palm, while the intersection of the veins may be more critical for subsequent feature extraction. According to the detected target key point coordinates, an affine transformation (such as rotation, scaling, translation, etc.) is used to align the palm area to a standard position and posture, which usually involves calculating a transformation matrix that can map the original palm area to the aligned position. After alignment, the alignment effect can be verified by calculating the alignment error or performing a visual inspection. If the alignment effect is not good, the regression detection algorithm or transformation parameters may need to be adjusted.

[0080] 203. Preprocess the palm vein vector feature information to obtain a similar feature set matching the palm vein vector feature information, and match the target search results based on the similar feature set.

[0081] Preprocessing can include routine operations such as feature standardization and normalization to ensure that the feature vectors between different samples are consistent when compared. The same feature set is a set of features that are similar to or belong to the same category as the currently input palm vein vector feature information that has been stored in the database. It often represents the palm vein features of different users and is used for subsequent identity authentication or recognition processes. Use an appropriate matching algorithm (such as distance measurement, similarity score, etc.) to compare the input palm vein vector feature information with each feature vector in the same feature set. Based on the matching results, determine the closest or most similar feature vector, and derive the target search result based on this. In the identity authentication scenario, this may mean confirming the user's identity; in the recognition scenario, it may mean finding the user information corresponding to the input palm vein image from the database.

[0082] Furthermore, the palm vein vector feature information is classified to obtain at least one similar feature set whose similarity with the palm vein vector feature information is greater than a set threshold; the weighted distance between the palm vein vector feature information and each similar feature set is calculated respectively; and the target search results are matched based on the weighted distance.

[0083] Specifically, the first distance between the palm vein vector feature information x and the similar feature set is calculated according to Formula 1, and each feature node in the similar feature set is obtained; Formula 1 is: Wherein, d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information. The second distance between the palm vein vector feature information and each feature node is calculated according to Formula 2; Formula 2 is: Where d2 is the second distance; the weighted distance is calculated based on formula three: d=N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M.

[0084] Furthermore, matching the target search results based on the weighted distance includes: screening the target feature set from at least one similar feature set according to the weighted distance; and matching the feature node with the smallest second distance in the target feature set as the target search result according to Formula 2.

[0085] In the process of matching the palm vein vector feature information in the embodiment, preprocessing is first performed, that is, unsupervised classification is performed on the collected palm vein vector feature information. The similarity between each vector in the vector set is pre-calculated, and the similarity relationship between the vectors is maintained in the form of a search tree. Then each palm vein vector feature information is used as a leaf of the search tree, that is, a node. The leaves that are close to each other are connected to form a subtree g. Formula 1 calculates the distance between the node and all subtrees g. Then the distance between the node and the currently stored leaves of each subtree is calculated, which is d2 calculated by Formula 2. According to the weight distance d, it is judged that the features (nodes) collected this time are inserted into a certain subtree.

[0086] The subtree is a set of similar feature vectors described in the embodiment, which may be one or more. In the case of multiple sets, a closest set of similar feature vectors is selected from the multiple sets according to the calculated weight distance, that is, γ is the distance threshold, forming a new classification search tree of palm vein vector features, and updating the subtree entry root node according to d2. Later, during palm vein recognition, the palm vein vector feature is input into the vector search engine to search the existing palm vein vector feature classification search tree. The weight distance d is calculated based on the entry root node root obtained in the previous preprocessing to find the most similar search tree. Since the palm vein feature vector dimension is low, the search tree is more effective than the search graph. At the same time, the multi-core parallel strategy is fully utilized in the pre-search processing, and the CPU multi-core of the server is enabled to search multiple existing classification search trees concurrently, so the search speed is greatly improved. This allows the embodiment to achieve the purpose of fast extraction speed and low memory usage. After that, the embodiment also performs secondary matching. After the pre-search finds the corresponding classification search tree through the weight distance, the distance d2 of formula 2 is used for secondary clustering search to obtain more accurate matching results, and the closest feature vector corresponding to the minimum distance is found. The identity recognition result is returned to the terminal module according to the matching result to complete the matching.

[0087] Embodiment 3

[0088] See also Figure 3 , Figure 3 Schematic diagram of the structure of a palm vein recognition device suitable for large throughput disclosed in an embodiment of the present invention. Figure 3 As shown, the palm vein recognition device suitable for large throughput may include: an image acquisition module 301, an information processing module 302 and a feature matching module 303, wherein the image acquisition module 301 is used to acquire palm vein image data in response to an image acquisition instruction; the information processing module 302 is used to process the palm vein image data to obtain palm vein vector feature information; the feature matching module 303 is used to pre-process the palm vein vector feature information, obtain a similar feature set matching the palm vein vector feature information, and match the target search result based on the similar feature set.

[0089] The information processing module 302 includes a recognition submodule: used to recognize the palm vein image data to obtain the palm area; an extraction submodule: used to align the palm area and extract the palm vein vector feature information of the palm area. Further, the recognition of the palm vein image data to obtain the palm area includes: performing binarization calculation on the palm vein image data to obtain the palm size, and obtaining the palm area based on the palm size. Correspondingly, the alignment of the palm area includes: performing regression detection on the palm area to detect the target key point coordinates in the palm area; and aligning the palm area based on the target key point coordinates.

[0090] The feature matching module 303 specifically includes: a classification submodule, a calculation submodule and a matching submodule, wherein the classification submodule is used to classify the palm vein vector feature information to obtain at least one similar feature set whose similarity with the palm vein vector feature information is greater than a set threshold; the calculation submodule is used to respectively calculate the weight distance between the palm vein vector feature information and each similar feature set; the matching submodule is used to match the target search results based on the weight distance.

[0091] Specifically, the weighted distance between the palm vein vector feature information and each similar feature set is calculated separately, including: calculating the first distance between the palm vein vector feature information x and the similar feature set according to formula 1, and obtaining each feature node in the similar feature set; formula 1 is: d1=(xu)S -1 (xu)', where d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information. The second distance between the palm vein vector feature information and each feature node is calculated according to Formula 2; Formula 2 is: Where d2 is the second distance; the weighted distance is calculated based on formula three: d=N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M.

[0092] Matching the target search result based on the weighted distance includes: screening the target feature set from at least one similar feature set according to the weighted distance; and matching the feature node with the smallest second distance in the target feature set as the target search result according to Formula 2.

[0093] Embodiment 4

[0094] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 4 As shown, the electronic device may include:

[0095] A memory 401 storing executable program codes;

[0096] a processor 402 coupled to the memory 401;

[0097] The processor 402 calls the executable program code stored in the memory 401 to execute part or all of the steps in the palm vein recognition method applicable to large throughput in the second embodiment.

[0098] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the palm vein recognition method applicable to large throughput in the second embodiment.

[0099] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the palm vein recognition method applicable to large throughput in the second embodiment.

[0100] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the palm vein recognition method suitable for large throughput in embodiment 2.

[0101] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily 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 the present invention.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0103] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0105] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0106] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0107] The palm vein recognition system, method, device, electronic device and storage medium suitable for large throughput disclosed in the embodiments of the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A palm vein recognition system suitable for large throughput, characterized in that: include: A server, an image collector and an image detection module, wherein the image collector collects palm vein image data, the image detection module is used to identify the palm vein image data to obtain a palm area, align the palm area, and extract palm vein vector feature information of the palm area and send it to the server; the server is used to classify the received palm vein vector feature information to obtain a similar feature set whose similarity with the palm vein vector information is greater than a set threshold, calculate the weighted distance between the palm vein vector feature information and other features in the similar feature set, and match the target search results based on the weighted distance.

2. A palm vein recognition method suitable for large throughput, characterized in that: include: Acquiring palm vein image data in response to an image acquisition instruction; Processing the palm vein image data to obtain palm vein vector feature information; The palm vein vector feature information is preprocessed to obtain a similar feature set matching the palm vein vector feature information, and the target search result is matched based on the similar feature set.

3. The palm vein recognition method according to claim 2, characterized in that: The processing of the palm vein image data to obtain palm vein vector feature information includes: Recognize the palm vein image data to obtain a palm area; The palm area is aligned and the palm vein vector feature information of the palm area is extracted.

4. The palm vein recognition method according to claim 3, characterized in that: The step of identifying the palm vein image data to obtain a palm area includes: Binarizing the palm vein image data to obtain a palm size, and obtaining a palm area based on the palm size; Accordingly, the alignment process for the palm area includes: Performing regression detection on the palm area to detect the coordinates of target key points in the palm area; The palm area is aligned based on the target key point coordinates.

5. The palm vein recognition method according to claim 2, characterized in that: Preprocessing the palm vein vector feature information to obtain a similar feature set matching the palm vein vector feature information, and matching the target search results based on the similar feature set, including: Classifying the palm vein vector feature information to obtain at least one similar feature set having a similarity with the palm vein vector feature information greater than a set threshold; Calculate the weighted distance between the palm vein vector feature information and each similar feature set respectively; The target search results are matched based on the weighted distance.

6. The palm vein recognition method according to claim 5, characterized in that: Calculate the weighted distance between the palm vein vector feature information and each similar feature set, including: The first distance between the palm vein vector feature information x and the similar feature set is calculated according to Formula 1, and each feature node in the similar feature set is obtained; Formula 1 is: Among them, d1 is the first distance, u is the mean of all features in the same feature set, S is the variance of all features in the same feature set, and x is the palm vein vector feature information. The second distance between the palm vein vector feature information and each feature node is calculated respectively according to Formula 2; the Formula 2 is: Where d2 is the second distance; The weighted distance is calculated based on Formula 3: d=N / α×d1+M / β×d2, where d is the weighted distance, N is the number of similar feature sets, M is the number of feature nodes in a similar feature set, α is the distance weight of N, and β is the distance weight of M.

7. The palm vein recognition method according to claim 6, characterized in that: The matching target search result based on the weighted distance includes: Filtering a target feature set from at least one similar feature set according to a weighted distance; According to Formula 2, the feature node with the second smallest distance in the target feature set is matched as the target search result.

8. A palm vein recognition device suitable for large throughput, characterized in that: include: Image acquisition module: used for acquiring palm vein image data in response to an image acquisition instruction; Information processing module: used for processing the palm vein image data to obtain palm vein vector feature information; Feature matching module: used to pre-process the palm vein vector feature information, obtain a similar feature set that matches the palm vein vector feature information, and match the target search results based on the similar feature set.

9. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the palm vein recognition method suitable for high throughput as described in any one of claims 2 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the palm vein recognition method applicable to high throughput as claimed in any one of claims 2 to 7.

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