102 results about "Palmar vein" patented technology

The invention relates to the technical field of vein recognition, in particular to a Bi-GRU vein recognition method based on an attention mechanism. Comprising the steps that a Bi-GRU model based on an attention mechanism is established, and the Bi-GRU model is mainly composed of a down-sampling layer, a feature coding module, a time attention module, a scale attention module and a fusion module; the down-sampling layer maps images in the multi-view finger vein image sequence into characteristic spectrums of multiple scales, and the characteristic coding module divides each characteristic spectrum into N characteristic blocks and converts the N characteristic blocks into characteristic vectors; the time attention module learns a time dependency relationship between the characteristic spectrums under the same scale; scale relation dependence is learned in the scale attention module, and the fusion module fuses the characteristic spectrum from the two dimensions of scale and time. And step 2, performing classification and identification of vein images by using the trained Bi-GRU model based on the attention mechanism. The problem that in the prior art, the accuracy of an identification method based on a multi-view finger vein image sequence needs to be improved is solved.

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.

The present invention relates to the technical field of computer vision, and proposes a finger and palm vein authentication method combining CNN and Transformer, comprising the following steps: obtaining a finger and palm vein image to be authenticated; extracting finger and palm vein features to be authenticated from the finger and palm vein image to be authenticated by using a deep learning model for extracting finger and palm vein features; wherein the deep learning model comprises: a deep learning model composed of CNN and Transformer; performing similarity measurement on the finger and palm vein features to be authenticated and the finger and palm vein features registered in a preset database, if the similarity between the finger and palm vein features to be authenticated and the registered finger and palm vein features is greater than a preset threshold, then the finger and palm vein authentication is successful, otherwise, the finger and palm vein authentication fails.

The embodiment of the invention provides a finger vein collecting device, which comprises a shell, a finger vein collecting device and a finger vein collecting device, the flexible optical sensor is used for performing vein detection on the finger placed in the accommodating groove; the sensing module comprises a first light source and a proximity sensor, the first light source is used for emitting sensing light, and the proximity sensor is used for outputting a first preset signal under the condition that the frequency of reflected light meets a first preset condition; the light supplementing module comprises a second light source used for emitting reinforcing light and an ambient light sensor, and the ambient light sensor is used for outputting a second preset signal under the condition that the intensity of the ambient light meets a second preset condition; the control circuit board is configured to respond to a first preset signal and control the ambient light sensor to be turned on; and in response to a second preset signal, controlling the second light source to be turned on. According to the technical scheme provided by the embodiment of the invention, the infrared light can be intelligently reinforced while the approach of the finger of the user is monitored, so that the acquisition quality of the finger vein image is improved.

The present invention discloses a handle-type finger vein smart lock in the mechanical field, including a mechanical unlocking structure, an electronic lock control component and a finger vein component; the mechanical unlocking structure, the electronic lock control component and the finger vein component are all configured in the smart lock; the electronic lock control component is used to realize the locking control and unlocking control of the smart lock; the finger vein component is used to identify and process the finger vein information, and when the finger vein verification is passed, the motor is driven to unlock; the mechanical unlocking structure is used to realize the locking control and unlocking control of the smart lock in the electronic lock control component. The present invention is configured with a mechanical unlocking structure, an electronic lock control component and a finger vein component, which can have a dual mode of fingerprint and password for safe locking. At the same time, when the smart lock loses power, at this time, the mechanical unlocking structure can be used to open and lock the smart lock to prevent the phenomenon that the smart lock cannot be opened when the power of the smart lock disappears.

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.

The invention relates to the technical field of biological recognition, and relates to a biological recognition method and device based on local coding, equipment and a medium, the method comprises the steps that first information and second information are acquired, the first information comprises a finger vein image of a target object, and the second information comprises a palmprint image of the target object; respectively preprocessing the first information and the second information to obtain first image information and second image information; feature extraction is carried out on the first image information and the second image information through a local coding method, a first feature vector and a second feature vector are obtained, the first feature vector comprises finger vein local coding features, and the second feature vector comprises palm print image local coding features; the target object is recognized according to the first feature vector and the second feature vector, the implementation process of multi-modal biological recognition is effectively simplified, and the recognition efficiency is improved while the recognition precision is ensured.

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 invention belongs to the technical field of identity recognition, and particularly discloses an identity recognition device and method based on multi-mode hand veins. The device comprises a vein image acquisition device and a computer device, and an improved UNet network and a multi-mode fusion network CVit are deployed on the computer device. The vein image acquisition equipment is multi-mode acquisition equipment and can acquire hand back and finger vein images at the same time, in addition, the invention further provides a self-adaptive dimming algorithm, proper illumination intensity is adjusted according to different palm thicknesses, and the problem that veins are not clear due to the palm thicknesses is solved. The ROI of any vein image can be extracted by the improved UNet network, so that the development cost is greatly reduced. According to the multi-modal fusion network CVit, VGG16 is used as a backbone network, features between two modals are fully extracted, the fusion part uses an attention mechanism in improved Transform to extract features associated with the two modals, the two sets of features are fused, and the effectiveness and robustness of vein recognition are improved.

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 finger vein recognition method based on compact binary coding learning, and relates to the technical field of mode recognition and image processing. The method comprises the following steps: extracting a finger vein texture difference vector, learning a mapping function, mapping and integrating features, calculating and splicing histograms block by block, matching features, and identifying finger veins. Existing algorithms have two main defects: (1) most of current methods highly depend on a rich priori knowledge base, which not only increases the complexity of implementation, but also is likely to be significantly influenced by adverse factors; and (2) the sample size of the existing finger vein database is small, and the finger vein recognition method based on deep learning is limited in practical application. According to the method, the recognition accuracy of finger vein recognition can be effectively improved under the condition of few samples, and meanwhile, the adaptability and generalization are high. Therefore, the method 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.

The utility model relates to the field of finger vein safety boxes, in particular to an emergency key hiding structure of a finger vein safety box, which comprises a finger vein safety box body and an emergency key, a finger vein scanner and an emergency lock hole are arranged at the front end of the finger vein safety box body in a matched mode, and a base is rotatably arranged at the bottom of the finger vein safety box body through a connecting assembly. The finger vein safety box has the advantages that the emergency key can be conveniently placed below the finger vein safety box body in a hidden mode, the hiding effect is better, the safety box can be used for storing the emergency key in a hidden mode, and the safety box is convenient to use. And the emergency key is convenient to take and place, and is convenient and fast to use.

The invention belongs to the technical field of finger vein recognition, and particularly relates to a single-sample finger vein recognition method. The method comprises the following steps: step 1, training a generative network G for generating through adversarial training of the generative network G and a discrimination network D by using a single finger vein image training sample of each user and a multi-scale and multi-direction transformation result of the training sample; 2, adversarial training is carried out on the recognition model for the finger vein image, in the first round of training, random noise Z is input into a trained generative network G to obtain a pseudo sample, the pseudo sample and a real training sample jointly form a training sample set, the finger vein image recognition network is trained according to the training sample set, and parameters of the recognition model are adjusted in the training process; and step 3, identifying a finger vein image by using the finger vein image identification network trained in the step 2. Through the model provided by the invention, the demand of training data can be reduced, and the efficiency of the model is improved.

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.