21 results about "Palmar vein" patented technology

This invention relates to a method for authenticating maintenance personnel in a power monitoring system based on finger vein recognition. The method includes: acquiring finger vein images of maintenance personnel for liveness detection; generating a baseline template if successful; encrypting the baseline template with the maintenance personnel's joint identifier to generate ciphertext, and splitting it into multiple ciphertext fragments for separate storage; responding to an operator's request to operate the system, acquiring the operator's finger vein images in real time for liveness detection; extracting real-time feature vectors based on the finger vein images if successful; obtaining ciphertext fragments of the corresponding operator's personnel identifier and operating account from multiple storage nodes, reassembling and decrypting them to obtain the corresponding baseline template; responding to a first matching degree between the real-time feature vector and the corresponding baseline template satisfying a preset condition, calculating a fused feature vector based on the baseline template and the real-time feature vector; and determining whether the operator is allowed to operate the power monitoring system based on a second matching degree between the fused feature vector and the baseline template.

This paper proposes a method for detecting finger vein spoofing attacks based on capsule networks. Capsule networks are not only suitable for small-sample finger vein datasets, but also, by replacing neurons with vector-represented capsules on top of CNNs, they can better handle spatial information such as relative position and angle, enhancing the network's adaptability to finger offset and rotation scenarios. A Bayesian routing algorithm is proposed, incorporating the differential entropy of the capsules as a consideration in calculating activation values. During final classification, feature capsules with high activation probabilities and high concentration are selected, which helps improve the accuracy of classifying genuine and fake veins. By simulating the uncertainty of capsule parameters, training errors can be reduced, improving recognition accuracy.

The utility model discloses a finger vein recognition mechanism which is used for reducing the influence of dust in the environment on a finger vein recognition area and improving the recognition rate. The device comprises a finger vein assembly, a track box and a sliding track structure, a containing space is formed in the track box, the finger vein assembly partially penetrates through the track box and is located in the containing space, and the side wall of the finger vein assembly is movably connected with the inner wall of the containing space through the track sliding track structure; the finger vein assembly comprises a working position and a storage position, and at the storage position, a recognition module on the finger vein assembly is located in the containing area; and at the working position, the finger vein assembly is controlled to slide along the sliding track structure, so that the upper end of the finger vein assembly slides out of the accommodating area.

The invention discloses a finger vein recognition method and system based on an improved deep learning network, and aims to solve the problems of low segmentation precision, unstable classification and high retraining cost, proposes GAN + CLAHE preprocessing, and constructs an R-LAB segmentation network which takes Xception as an encoder and introduces space and channel attention; designing improved Xception feature extraction containing a global average pooling and residual error compensation structure; and a cascade KNN based on cosine similarity is adopted to complete personnel identification and support dynamic registration. The system comprises a data preprocessing module, a segmentation module, a feature module, a classification module and an integration module, and comprehensive improvement of precision, efficiency and flexibility is achieved.

A finger vein recognition method based on compact binary coding learning relates to the technical field of pattern recognition and image processing. The method comprises the following steps: extracting finger vein texture difference vector, learning mapping function, mapping and feature integration, calculating and splicing block histogram, feature matching, finger vein recognition. The existing algorithm has two main defects: (1) most current methods highly depend on rich prior knowledge base, such dependence not only increases the complexity of implementation, but also is easily affected by adverse factors; (2) the sample quantity of existing finger vein database is small, and the finger vein recognition method based on deep learning is limited in practical application. The present application can effectively improve the recognition accuracy of finger vein recognition under the condition of few samples, and has strong self-adaptability and generalization. Therefore, the present application has certain application value and significance.

The invention discloses a two-view finger vein recognition method and system based on multi-scale Mama, and relates to the technical field of biological feature recognition. The method comprises the steps that a multi-scale Mama feature extraction module is used for conducting feature extraction on a finger vein image of each view angle, and multi-scale features are obtained; enhancing the multi-scale features in each view angle to obtain enhanced features of the two view angles; performing feature interaction on the enhanced features of the two view angles by using a Mamba feature interaction model to obtain interaction features of the two view angles; and splicing the interaction features of the two visual angles, and predicting the finger vein image category. According to the method, the problems of information loss and attitude sensitivity existing in single-view-angle recognition are solved, and the recognition performance and robustness of a recognition system are further improved.

The embodiment of the invention provides a finger vein recognition technology (FIVRT) based on deep learning, and relates to a finger vein recognition method based on deep learning. The finger vein recognition technology is one of biological feature recognition technologies, has the characteristics of living body recognition, non-replicable in-vivo features, uniqueness, stability, non-cracking and the like, and has a higher safety coefficient than fingerprint recognition and face recognition. According to the embodiment of the invention, the finger vein feature values are extracted by using the FingerCavaNet network model and mapped into the feature vectors in the Euclidean space, the similarity between the two pieces of finger vein data is measured through the Euclidean distance between the two feature vectors, and the smaller the Euclidean distance is, the higher the similarity is. Therefore, personal identity authentication and identity confirmation are realized. The shape of the finger vein has uniqueness and stability, that is, the finger vein images of each person are different, the vein images of different fingers of the same person are also different, and the vein shape of a healthy adult does not change any more, so that a basis is provided for the finger vein to be used for identity recognition.

This application relates to the field of fingerprint recognition technology, and particularly to a fingerprint recognition method, device, electronic device, and storage medium. The method includes: responding to a user's pressing operation, obtaining a confidence level of the sweat pore detection result based on the sweat pore information of the original fingerprint image of the fingerprint to be identified, and obtaining a confidence level of the vein detection result based on the finger vein characteristics; generating a time-series detection result based on the first start time of the sweat pore detection and the second start time of the finger vein detection, and calculating a comprehensive confidence level by combining the confidence levels of the sweat pore detection result and the vein detection result, thereby determining whether the fingerprint to be identified is a live fingerprint. This solves the problems of related technologies relying on single physical features, difficulty in identifying highly realistic fake fingerprints, and lack of spatiotemporal consistency analysis methods. By integrating multimodal biometric signals such as sweat pore features, finger vein features, and time-series consistency verification for comprehensive discrimination, effective prevention of forged fingerprint attacks is achieved.

The application discloses a kind of finger vein identification method, device, computer readable storage medium and equipment, belong to the field of biometric identification.It includes: obtaining finger vein image;Calculate the gradient information of finger vein image, according to gradient information, find the finger boundary using greedy strategy;Detect finger joint on finger vein image, and extract the effective area of finger vein according to the finger joint and finger boundary;Through Gabor filter, the effective area is extracted, and the finger vein feature is obtained;Set sliding window on the finger vein image, and the finger vein feature of the finger vein image in sliding window is compared with the corresponding position of the finger vein feature template Similarity, find the finger vein image in the highest similarity sliding window as the maximum matching area;The similarity of maximum matching area and the corresponding position of finger vein feature template is used as matching score.The application can realize stable and efficient finger vein identification for different devices and different collection environments.

This application relates to the field of biometric recognition and image processing technology, and discloses a method, model, device, and medium for finger vein recognition based on a lightweight attention model with few samples. The method includes: acquiring raw finger vein images and extracting regions of interest (ROIs); preprocessing the ROI images; performing data augmentation on the training set images; constructing an LA-VGG network based on a VGG-19 network, introducing CA attention, PReLU, and cross-layer fusion to enhance subtle textures; introducing a loss function with additive angular margins during the training phase; training the LA-VGG network to obtain a finger vein recognition model; inputting the image to be recognized into the model, and outputting the identity recognition result. This invention can effectively alleviate the overfitting problem under small sample conditions, reduce the number of model parameters and computational complexity, improve the extraction capability of key finger vein texture features, and is suitable for deployment and application in resource-constrained devices and complex environments.

To provide a USB security key with high security. [Solution] The USB security key 1 comprises a circuit board 11 fixed by structural members inside an outer case 10, an embedded MCU main control chip 12 mounted on the circuit board 11 and containing a secure element 21, a palmar vein authentication module 13 connected above the circuit board 11 and electrically connected to the embedded MCU main control chip 12, and a USB data interface 14 provided at the end of the circuit board 11 and electrically connected to the embedded MCU main control chip 12. The palmar vein authentication module 13 acquires a palmar vein image and generates an authentication result signal, the embedded MCU main control chip 12 receives the authentication result signal, generates authentication data based on the authentication result signal, packages the authentication data into authentication response data according to a predetermined protocol, and outputs it via the USB data interface 14.

The application discloses a self-adaptive dual-mode identity recognition method for finger vein and external finger joint vein, and belongs to the field of biometric recognition technology.The method comprises the following steps: S1, collecting finger vein and external finger joint vein images; S2, pre-processing the finger vein and external finger joint vein images; S3, quality evaluation of the finger vein and external finger joint vein images and giving corresponding scores, and determining whether the images meet the requirements according to the scores; if the images meet the requirements, the next step is performed, otherwise, re-collection is prompted; S4, advantage feature coefficient assignment according to the quality of the corresponding finger vein and external finger joint vein images; S5, using an improved SIFT algorithm for the finger vein image and using a PHASH algorithm for the finger joint vein image to perform similarity matching with features in a database to obtain corresponding similarity; S6, combining the corresponding weight and the similarity through a weighted similarity function to obtain a corresponding weighted matching degree; S7, fusing the finger vein and finger joint vein weighted matching degrees through a fusion function to obtain a final fusion matching degree; and S8, threshold value judgment.The application has high recognition accuracy and robustness.

The present disclosure relates to a finger vein recognition system. The finger vein recognition system includes an IR camera, a handle having a sensor and an array of IR LEDs, and a microprocessor for controlling the array of LEDs and processing image signals received from the camera. The emission of each LED in the array of LEDs can be varied based on the position of the user's finger and the environment, which improves the quality of the image captured by the IR camera. The sensor detects the proximity of the user and activates the system.

The invention discloses a finger vein recognition method and device based on multi-domain feature enhancement and fusion, and relates to the technical field of biological feature identity recognition. The method comprises the steps of obtaining a to-be-recognized finger vein image; performing spatial domain feature enhancement, gray domain feature enhancement and frequency domain feature enhancement on the image to obtain a processed image; extracting the LBP feature of the processed image in a blocking manner to obtain an LBP feature vector of each block image; generating a geometric feature vector of each feature point in the processed image based on a morphological processing method, a feature point extraction method and a histogram statistical method; fusing the geometric feature vector of each feature point with the LBP feature vector of the block image where the feature point is located to obtain a fused feature vector; and based on an improved circular neighborhood position distance matching method, determining the category of the image by using the fused feature vectors of the feature points, the category of the database image and the fused feature vectors of the feature points. The identification performance can be improved.

The application discloses a finger vein recognition method and system based on dynamic balance of false recognition rate and false rejection rate, and the method comprises the following steps: obtaining a to-be-recognized finger vein image, and performing image preprocessing and ROI extraction processing to obtain an ROI region of the finger vein image; a finger vein recognition model is constructed, three-stage hybrid training is performed in combination with the ROI region of the finger vein image, and a trained finger vein recognition model is obtained; a balanced error rate evaluation index is constructed to evaluate the trained finger vein recognition model, and an evaluated finger vein recognition model is obtained; and finger vein recognition is performed on the to-be-recognized finger vein image based on the evaluated finger vein recognition model, and a finger vein recognition result is obtained. The application can meet the finger vein recognition requirements in multiple scenes and improve the finger vein recognition accuracy. The application can be widely applied to the technical field of finger vein recognition as a finger vein recognition method and system based on dynamic balance of false recognition rate and false rejection rate.

The invention discloses a finger vein recognition method and device, a storage medium and equipment, and belongs to the field of biological feature recognition. The method comprises the following steps: firstly, carrying out rotation alignment on a finger vein image through a finger center line, obtaining a region of interest, then obtaining an optimal matching region through a sliding window mode based on Gabor binary coding, and finally, carrying out local feature descriptor extraction and matching on the optimal matching region. According to the invention, the finger vein recognition of the local feature point and feature descriptor method is carried out through the finger midline registration and sliding matching strategy, the invalid comparison is greatly reduced, the effective local descriptor is concerned, and the matching efficiency is greatly improved.

The invention discloses a user authentication method, computer equipment and system based on a finger vein image, and relates to the technical field of image processing, and the method comprises the steps: obtaining a finger vein image of a user; the finger vein image is preprocessed; inputting the preprocessed finger vein image into a pre-trained multi-noise identification model to identify an image noise type; performing noise reduction processing on the finger vein image according to the image noise type; performing feature extraction on the finger vein image subjected to noise reduction processing so as to generate an image embedding feature vector; comparing the image embedded feature vector with a preset feature vector template; if the similarity between the image embedded feature vector and a feature vector template is greater than a preset similarity reference, authorizing the user to access; and if the similarity between the image embedded feature vector and the feature vector template is smaller than or equal to the similar reference, refusing the user access. By adopting the method and the device, the noise type in the finger vein image can be effectively identified, and different targeted measures are adopted for performing accurate noise reduction aiming at different image noise types, so that the authentication accuracy is improved.

The application provides a finger vein recognition method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining a to-be-recognized image; performing feature extraction on the to-be-recognized image through a preset finger vein feature extraction model to obtain a finger vein feature; wherein the finger vein feature extraction model comprises a nonlinear feature extraction network; the nonlinear feature extraction network is obtained based on a nonlinear convolution kernel; performing finger vein recognition according to the finger vein feature to obtain a recognition result. The nonlinear feature network with less to-be-optimized parameters is used to replace the conventional linear feature network, thereby reducing the calculation cost and training time and making the model easier to train. Moreover, the nonlinear convolution network is applied to the finger vein recognition, the nonlinear parameter dependent convolution kernel provides additional nonlinear features for the finger vein recognition, the feature extraction capability of the network is stronger, and the recognition accuracy is higher.

The application discloses a palm vein effective area extraction and correction method, which comprises detecting a rectangular palm vein effective area in a palm vein image and locating four finger root points in the palm vein effective area; and adjusting the palm vein effective area to have a finger direction upward, and performing orientation rotation correction on the palm vein effective area according to the structural characteristics of left and right hands, so that a point between an index finger root and a middle finger root and a point between the middle finger root and a ring finger root are located on a same horizontal line parallel to an X axis. The application also provides an identification method and a computer readable storage medium using the palm vein effective area extraction and correction method. The palm vein effective area extraction and correction method can extract the palm vein effective area in the palm vein image to exclude interference areas outside a palm center area, and unify the palm orientation angle in the palm vein effective area, so that the influence of unnecessary interference areas and angles on a subsequent palm vein identification process is eliminated.

The invention discloses a finger vein image recognition method and device. The method comprises the steps that a plurality of frames of finger vein original images are collected through an image sensor; preprocessing the finger vein original image, extracting an image containing finger vein information, and dividing the image into a training set, a verification set and a test set according to a preset proportion; the YOLOv5 target detection model is improved, and an improved YOLOv5 target detection model is obtained; the YOLOv5 is improved, specifically, on the basis of a YOLOv5 target detection model, a multi-branch topology module comprising a convolutional network structure is arranged; training the improved YOLOv5 target detection model through the training set to obtain a trained target detection model; inputting a to-be-recognized image into the trained target detection model to obtain a recognition result; according to the invention, through optimizing the links of image acquisition, preprocessing, feature extraction and matching, the recognition accuracy and stability are significantly improved.

The application discloses an identity verification method and device, electronic equipment and storage medium, and belongs to the field of image processing. The identity verification method comprises the following steps: acquiring a first finger vein image of a first user; performing image cutting on the first finger vein image to obtain a plurality of first finger vein sub-images of different regions, and the image cutting strategy of the first finger vein image is determined based on the texture offset degree of the first finger vein image; acquiring a first global feature vector of the first finger vein image, and acquiring a plurality of first local feature vectors of the plurality of first finger vein sub-images, wherein the first local feature vectors correspond to the first finger vein sub-images one by one; comparing the first global feature vector and the plurality of first local feature vectors with identity verification information of a second user registered in an identity library to obtain an identity verification result of the first user, and the identity verification information of the second user comprises a second global feature vector and a plurality of second local feature vectors. The method improves the finger vein recognition accuracy and efficiency.