56 results about "Palmar vein" patented technology

The invention provides a biological feature recognition method based on finger vein image layering, and relates to the technical field of biological recognition, and the method comprises the steps: collecting a finger vein image of a to-be-recognized person, carrying out the preprocessing of the finger vein image, obtaining a first recognition image of finger vein layering, extracting a region of interest of a finger through an edge detection algorithm, and carrying out the recognition of the finger vein layering; a continuous edge curve is formed through double-threshold detection, after feature region segmentation is achieved, surface vein texture features are extracted, deep vein geometric features are extracted, then finger vein index feature values are generated, and the finger vein index feature value matching difference of a person to be recognized and a known person is calculated through a similarity measurement method; and the maximum matching difference value is screened, and whether the identities are matched or not is judged according to a set matching threshold value, so that high-precision identity authentication is realized. According to the invention, through multi-level feature fusion and accurate matching, the accuracy and reliability of finger vein identity verification are improved.

The present invention belongs to the field of biometrics, and more particularly relates to a method and device for finger vein recognition and detection that integrates texture and liveness features. The method comprises first installing an imaging device, a plurality of beam splitters, a plurality of reflectors, a filter, a light source module, and a processor in a preset position; placing the finger to be detected in the predetermined position to obtain multiple near-infrared venography images; then performing pixel-level registration to crop images of regions of interest with varying light intensities, combining these images into a multi-channel image; utilizing the known light intensity ratios of the multi-channel vein images and the camera's light response function to establish a finger transmission model; performing quantitative calculation of the transmission light offset for each point on the finger image; and comparing the registered finger transmission light offset map with the transmission offset map of the finger to be detected to determine whether the finger is the same and whether it is live. The present invention can address the problem of weak resistance to finger vein prosthesis attacks and video replay attacks encountered in the prior art.

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.

The present invention relates to the technical field of biometric feature processing and identification methods, and discloses a lightweight finger vein image recognition method for identifying the characteristics of specific finger veins, comprising the following steps: S1, acquiring a user's finger vein image; S2, calculating the finger's offset angle relative to the horizontal midline using an edge detection method, fitting the image midline to the horizontal midline through image rotation, and obtaining the required horizontal finger vein grayscale image by region-of-interest (ROI); S3, performing image enhancement by dividing the grayscale image into a certain number of grayscale squares of equal size and calculating the grayscale value histogram within the squares for equalization; S4, performing bilinear interpolation on the enhanced grayscale image under lightweight optimization, and then performing adaptive local threshold binarization to obtain a filtered finger vein image in the grayscale image. The present invention improves the security and efficiency of identity authentication and reduces the memory required, processor performance requirements, and production costs of the program in actual use.

The present invention discloses a method and device for binocular finger vein identity recognition. The method includes: calibrating the parameters of the left and right cameras; calculating two-dimensional left and right finger vein texture images; performing stereo rectification on the left and right finger vein texture images; obtaining finger vein texture point cloud data with a three-dimensional spatial topological structure; performing noise reduction processing on the finger vein texture point cloud data; constructing a model for cross-modal finger vein identity information representation with the collaboration of two-dimensional images and three-dimensional point clouds; calculating a combined representation vector of the fusion feature corresponding to the maximum correlation and the binocular finger vein feature attributes; constructing a finger vein feature database with identity identification attributes. According to the matching relationship between binocular infrared finger vein texture images, the present invention establishes a convolutional neural network model, converts two-dimensional finger vein texture images and three-dimensional finger vein texture point clouds into corresponding feature vectors, and constructs a combined representation of cross-modal finger vein feature information, thereby improving the identification ability of identity recognition.

This invention discloses a finger vein recognition method based on a joint loss and convolutional neural network. The method includes the following steps: acquiring a finger vein image, preprocessing the image, extracting the region of interest (ROI) of the finger vein image, and normalizing the image size; constructing a deep convolutional neural network for finger vein recognition, using a feature extraction network based on the ResNet34 architecture; training the model by using a joint loss function in Euclidean and cosine metric spaces to train the deep convolutional neural network so that the network model learns effective feature representations of finger veins; and determining a recognition threshold, using the false acceptance rate and false recognition rate as metrics, and finding the optimal recognition threshold through enumeration to achieve finger vein image recognition. The method is used for finger vein image feature extraction and matching, effectively performing finger vein identity recognition.

The present application discloses a finger vein sensor and an electronic device, belonging to the field of sensor technology. The finger vein sensor includes: a substrate, an infrared light source and an infrared detector. Since the distance between the first light-emitting element in the infrared light source and the sensing area is smaller than the distance between the second light-emitting element in the infrared light source and the sensing area. Therefore, the near-infrared light emitted by the first light-emitting element will travel a shorter distance to the object to be detected in the sensing area. When the object to be detected is the user's finger, the distance between the knuckles in the user's finger and the first light-emitting element is smaller, and the near-infrared light emitted by the first light-emitting element can travel a shorter distance to the knuckles, so that the effect of the vein distribution image at the joint is better. In addition, the thickness of the finger vein sensor is relatively small, which is conducive to integrating the finger vein sensor with other components in an electronic device, so that the electronic device integrated with the finger vein sensor is smaller in size.

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.

Facial vein and palm vein integrated equipment relates to the technical field of vein recognition equipment, and comprises a frame body, a camera, a recognition module and a control module, the camera comprises a facial vein acquisition camera and a palm vein acquisition camera, the facial vein acquisition camera is fixed on the front surface of the frame body, and the palm vein acquisition camera is fixed on the side surface of the frame body; the recognition module is mounted in the frame body and is in communication connection with the facial vein acquisition camera and the palm vein acquisition camera; the control module is installed in the frame body and is in communication connection with the identification module. The equipment integrates facial vein identity recognition and palm vein identity recognition, and is higher in flexibility and wider in use scene.

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.

1. The name of the product of this design: Palm vein mouse. 2. Purpose of this design product: used as a mouse to read palm vein information. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the key points of the design: 3D drawing of Design 1. 5. Designate Design 1 as the base design.

The utility model relates to the technical field of door locks, in particular to a vein recognition intelligent door lock front plate which comprises a door lock front plate body, the door lock front plate body comprises a base and a panel, the upper end of the panel is provided with a connecting block connected with the base, the rear side of the lower end of the panel is provided with a connecting column connected with the base, and the connecting column is used for installing a lock cylinder. A gap is reserved between the panel and the base, a finger vein module is arranged on the rear side of the upper end of the panel, an air outlet corresponding to the finger vein module is formed in the front side of the upper end of the base, an air inlet is formed in the side wall of the lower end of the base, a miniature air blower and an air duct are arranged in the base, the upper end of the air duct is communicated with the air outlet, and the lower end of the air duct is communicated with the miniature air blower. The micro air blower is communicated with the air inlet, a dehumidification switch is arranged on the front side wall of the panel, and a controller electrically connected with the micro air blower and the dehumidification switch is arranged in the panel. According to the utility model, the finger vein module can be quickly blow-dried when water vapor is condensed on the finger vein module, so that the condition of finger vein recognition failure is avoided.

The present application discloses a palm print palm vein fusion method, device and electronic device, belonging to the field of biometric identification technology. The method comprises: inputting a palm print RGB image and a palm vein IR image into a palm print palm vein fusion model, fusing the palm print RGB image and the palm vein IR image, and obtaining a palm print palm vein fusion feature map; the palm print palm vein fusion model is trained based on palm print RGB image samples and palm vein IR image samples. The present application fuses the palm print RGB image and the palm vein IR image through a palm print palm vein fusion model to generate a palm print palm vein fusion feature map, and performs biometric identification based on the palm print palm vein fusion feature map, thereby improving the accuracy and stability of biometric identification.

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 present invention discloses a finger vein recognition method based on feature fusion. First, the input finger vein image is successively subjected to finger edge extraction, finger region segmentation, finger rotation correction, and extraction of the region of interest of finger veins. Then, the histogram of oriented gradients features of the region of interest of finger veins is extracted, and the local phase quantization features of the region of interest of finger veins are extracted. Finally, on this basis, the principal component analysis algorithm is used to perform feature selection on the above two kinds of features respectively, and the joint sparse representation classification method is used to fuse and recognize the above histogram of oriented gradients features and local phase quantization features, so as to achieve the final identity recognition. This solution further improves the security and effectiveness of identity recognition, has the advantages of good reliability and high use value, and can be widely applied to fields such as access control systems and security.

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.

The present invention belongs to the field of automatic control, and particularly relates to a finger vein recognition method based on variable curvature Gabor convolution and a multi-branch structure, and a corresponding finger vein recognition system and finger vein verification device. The solution of the present invention first improves the traditional Gabor filter by adding variable curvature so as to extract information on different bending degrees in the vein curve. Secondly, the variable curvature Gabor function is constructed into a learnable VC-Gabor convolution layer, and the parameters of the VC-Gabor convolution layer are updated by using the backpropagation of the neural network, so that the directions, scales and curvatures of the learned VC-Gabor filters are more abundant, and complex manual parameter adjustment is avoided. Finally, a network model including a VC-Gabor convolution layer and an adaptive multi-branch structure is designed in combination with the convolution layer in the classical CNN network, and then the network model is used for rapid recognition of finger vein images. The present invention solves the defects of insufficient accuracy and poor robustness of the existing finger vein recognition methods.

The invention relates to a palm vein recognition method and device, computer equipment and a medium. The method comprises the following steps: collecting a first original image of a palm to be recognized under light supplement of a first infrared lamp; collecting a second original image of the palm to be recognized under the supplementary lighting of a second infrared lamp; wherein the infrared bands of the first infrared lamp and the second infrared lamp are different; unifying the background brightness of the first original image and the background brightness of the second original image, performing vein gray level comparison on the first original image and the second original image after the background brightness unification, and when it is determined that the vein gray level difference between the first original image and the second original image is greater than or equal to a preset gray level threshold value, performing vein gray level comparison on the first original image and the second original image; and recognizing the palm to be recognized as a real living palm. By adopting the method, the security of palm vein recognition can be improved.

A finger vein counterfeit detection method based on multi-feature fusion uses the difference in imaging quality between genuine and fake vein images to fuse the noise features and fuzzy features of the image to detect counterfeit attacks. This method is simple to implement. The original finger vein image can be used for subsequent identity recognition while realizing authenticity identification, reducing the consumption of additional computer resources; compared with the finger vein counterfeit detection method that only uses a single image texture feature, this method fuses the noise features and fuzzy features of the vein image, which can increase the distinction between genuine and fake vein images and help improve the accuracy of finger vein counterfeit detection; compared with the traditional direct series or parallel multi-feature fusion method, this method uses weighted feature fusion, takes into account the importance of each feature, reduces the mutual influence between features, and improves the generalization ability of the classifier.

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 invention relates to an intelligent cabinet multi-identity recognition information fusion method and system based on information entropy, and the method comprises the steps: carrying out the photographing of a human face image and a finger vein image through terminal equipment on an intelligent cabinet, and obtaining the feature vectors of the human face and the finger vein through a feature extraction algorithm; an improved deformable convolution algorithm is used for eliminating the measurement inconsistency of face features and finger vein features, and after feature alignment, the face features and the finger vein features are mapped to the same vector space and are connected in series to form a group of multi-identity feature vectors. And finally, judging whether the multi-identity feature vector is valid or not by using the lowest valid feature dimension obtained by the information entropy, outputting the valid feature vector, comparing the valid feature vector with a feature database, and outputting a recognition result. The face information and the finger vein information are comprehensively utilized, and the recognition precision and accuracy of the intelligent cabinet recognition system in a complex environment are effectively improved.

Provided in the present application are a feature extraction method and apparatus. In the present application, a plurality of finger vein images of the same finger which are captured at different angles are acquired, and then by means of the processing method in the present application, deformation of veins in each finger vein image, which deformation is caused by slight shaking of the finger, can be corrected on the basis of the plurality of finger vein images captured at the different angles, and thus finger vein features having relatively high accuracy are obtained, thereby facilitating an improvement in the accuracy of identity authentication performed by means of finger vein recognition.

The present invention discloses a palm print and palm vein fusion recognition method under missing modalities based on feature decoupling, which aims to solve the problems existing in the current multimodal fusion methods under missing modalities: (1) using a generative method, a generative model needs to be designed for each missing modality, resulting in a large overall model that is not suitable for practical applications; (2) most modality missing methods specify the missing modality, which limits flexibility. In view of the above problems, the present invention uses a simple network model, designs a palm print cross-modal generator or a palm vein cross-modal generator that decouples shared features and specific features using contrastive learning loss and inter-modality identity consistency loss, and constructs a palm print cross-modal generator or a palm vein cross-modal generator at the feature layer, and uses known modal features to complete the missing modal features. Different from the existing methods, the multimodal fusion method proposed in the present invention has excellent robustness, and can still obtain accurate recognition results when any modality is missing in the test set, meeting the needs of diverse application scenarios.

1. The name of the product of this design: Palm vein collection device. 2. Purpose of this design product: used to collect the user's palm vein features to identify and verify the user's identity information. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.