152 results about "Palm vein" patented technology

The present disclosure relates to a desktop safety box including a housing, a cover, a locking device, and a palm vein unlocking module. The housing defines an accommodating cavity and an opening communicated with each other. The cover is mounted on the housing and is capable of opening or closing the opening. When the locking device is in a closed state, a relative position between the cover and the housing is locked, and the opening is closed. When the locking device is in an unlocked state, a lock between the cover and the housing is released, and the opening is allowed to be opened. The palm vein unlocking module is mounted on the housing, electrically connected to the locking device, and configured to acquire palm vein information and drive the locking device to switch to the unlocked state.

The invention discloses a palm vein image generation method and device, a storage medium and electronic equipment, and relates to the technical field of information. The method comprises the steps that an initial noise image, the number of diffusion steps and condition control information are obtained, and the condition control information comprises an individual identity label, palm posture information used for simulating a real scene and illumination information; generating a condition control embedding vector according to the condition control information; the initial noise image, the diffusion step number and the condition control embedding vector are input into a preset diffusion model to predict noise corresponding to each time step, and the preset diffusion model comprises a linear layer, a cross attention layer and a multi-scale feature layer; and according to the predicted noise corresponding to each time step, gradually updating the image, and finally outputting a target palm vein image under the condition control information. According to the method and the device, the generated palm vein image data can be fit to various complex real scenes, so that the diversity and the practicability of the palm vein image data can be ensured.

The invention discloses a method and system for testing human body static electricity through identity recognition based on palm veins, and relates to the technical field of image recognized.The system comprises an image acquisition module, a feature fusion module, an identity recognition module, a static electricity testing module and a state display module.The microcirculation pulsation is detected through a time-space domain light intensity distribution model, and the human body static electricity is detected through a time-space domain light intensity distribution model; the light intensity change rate is quantified, so that a living body detection mechanism is established, accurate recognition is performed through a multispectral fusion technology, palm vein images are acquired by adopting multi-wavelength infrared light, the defect of single-band imaging is overcome, the anti-interference capability is improved, and the feature extraction precision is improved; according to the method, the light intensity parameter HSV and the color parameter RGB of the multi-source image are integrated, the fused image is constructed, through palm vein image feature recognition and matching, the identity of a tester is compared and judged, the vein texture recognition degree is enhanced, the recognition deviation is reduced, the safety level is high, the test time is short, and more reliable and accurate data analysis is provided for the enterprise big data era.

The invention discloses a novel graph contrast learning framework with mutual information maximization, and relates to the technical field of graph contrast learning, multi-modal learning and mutual information maximization. The framework comprises the following steps: constructing a nearest neighbor graph, spreading similar samples to data of a missing mode, optimizing a loss function, and performing clustering optimization. The existing method has two main problems: (1) the existing method is mostly suitable for complete data and has a poor processing effect on data of a missing mode; and (2) the current palmprint and palm vein fusion recognition technology does not completely capture the complex relationship between the palmprint and palm vein features, resulting in inaccurate clustering result. According to the method, the modal loss in the incomplete palm print and palm vein fusion recognition task is effectively reduced, and the clustering performance is enhanced. According to the method, the mutual information maximization loss function is introduced, the adaptability and robustness of the model to incomplete data are effectively enhanced, and the method has high recognition precision and clustering performance when processing incomplete multi-modal data.

The invention discloses a non-contact palm vein multi-mode identification system based on deep learning. The system comprises a multi-mode image acquisition and processing terminal, a mode matching and identification decision terminal, a safety protection and anti-counterfeiting terminal and an iterative optimization terminal. The multi-modal image acquisition and processing terminal is used for acquiring palm print and palm vein images, performing dynamic calibration, image quality enhancement and preprocessing, and outputting multi-modal images; the mode matching and identification decision terminal is used for carrying out feature extraction, multi-modal feature fusion, mode matching and identification decision and outputting a final matching result; the safety protection and anti-counterfeiting terminal is used for carrying out living body detection, anti-counterfeiting monitoring and data encryption; and the iterative optimization terminal is used for realizing model lightweight, knowledge distillation, end-to-end optimization and continuous learning. According to the invention, through deformation invariant feature extraction and cross-modal attention fusion, the recognition accuracy and environmental adaptability in a complex state are improved.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

The invention provides a palm vein dynamic capture and deep learning identity recognition terminal, which relates to the technical field of biological feature recognition, and comprises a dynamic capture module, a deep learning identity recognition module and an implementation processing and feedback system, the dynamic capture module collects multiple frames of palm vein images and combines a deep convolutional neural network and recurrent neural network hybrid model of the deep learning identity recognition module, abundant space and time sequence features can be extracted, a high-distinction-degree comprehensive feature vector is generated, meanwhile, vein features under the complex background are adaptively learned, and the recognition accuracy is improved. Recognition precision and robustness are remarkably improved, interference such as light changes and skin states is adapted, a palm is supported to move freely within a certain range, the palm does not need to be kept static deliberately, the time of the whole process from dynamic capture to identity recognition is shortened to be within one second through a high-performance embedded processor and an optimization algorithm in combination with a real-time processing and feedback system, and the recognition efficiency is improved. Therefore, the method is suitable for a rapid identity authentication scene.

The invention discloses a palm vein recognition method based on a domain adaptation strategy, and relates to the technical field of biological information recognition, identity authentication and palm recognition, and the method comprises the steps: 1, constructing a data set which comprises a source data set and a target data set, and employing all data in the source data set and part of data in the target data set as an initial training set, using other data in the target data set as an initial test set, then performing ROI extraction, segmenting the initial training set into a formal training set, and segmenting the initial test set into a formal test set; 2, designing an adversarial domain adaptation network; 3, pre-training is carried out on the source data set, so that the classification module can reach preset classification performance on the source data set; 4, adversarial training is carried out on the formal training set, so that the palm feature extraction module learns domain-invariant features, and when the training reaches a preset iteration frequency or a model threshold value, the training is ended; and 5, testing by using the formal test set.

The invention relates to the technical field of artificial intelligence, and discloses a palm vein recognition method and device based on human anatomy, equipment and a medium, and the method comprises the steps: obtaining a palm vein image sequence of a user; carrying out anatomical key point positioning processing on the palm vein image sequence to obtain anatomical key point coordinates of the palm vein image sequence; according to the anatomical key point coordinates, carrying out geometric correction processing on an ROI region of the palm vein image sequence to obtain a standardized ROI region sequence; performing multi-modal feature extraction processing on the standardized ROI region sequence to obtain vein texture features and hemodynamic features; and performing cross-modal fusion processing on the vein texture features and the hemodynamic features to obtain palm vein fusion features. According to the invention, the recognition rate and safety of palm vein recognition can be improved.

The present invention provides a palm vein edge refinement detection method that incorporates cross-self-attention. The method comprises: obtaining a palm vein image to be processed; inputting the image into a trained palm vein edge refinement detection network to obtain edge information. The palm vein edge refinement detection network comprises a feature extraction unit for extracting local details and global structure, a feature fusion unit for fusing features of different scales in space and channels, and a feature refinement unit for segmenting the fused features. The method first extracts local details and global features using the feature extraction unit, then uses the feature fusion unit to aggregate features of different scales in space and channels, and finally uses the feature refinement unit to specifically refine uncertain points in the initial prediction results, thereby achieving accurate segmentation of palm vein edges.

The present application belongs to the technical field of palm vein recognition, and particularly relates to a palm vein recognition data enhancement method and system based on adversarial learning, which utilizes a conditional deep convolutional generative adversarial network composed of a generator and a discriminator, and synchronously trains the generator and the discriminator; step 2 selects a palm vein classifier and the trained generator to form an adversarial network, step 3 performs multiple batches of adversarial training on the palm vein classifier based on the adversarial network, generates adversarial samples for increasing the loss of the classifier and training samples based on real palm vein recognition data to form an augmented data set in each batch of training, trains the classifier, and updates the parameters of the classifier; and updates the noise vector set required for the next batch of adversarial training. The present application reduces the requirements for computing and storage resources in data enhancement, and provides more high-quality training samples for palm vein recognition model training.

The utility model discloses a multi-mode palm vein recognition terminal machine which comprises a multi-mode palm vein recognition terminal machine body, the outer wall of the multi-mode palm vein recognition terminal machine body is fixedly connected with a shell, the top ends and the bottom ends of the two sides of the multi-mode palm vein recognition terminal machine body are respectively provided with a square plate, and the square plates are fixedly connected with the inner wall of the shell. The two sides of the multi-mode palm vein recognition terminal machine body are each provided with two guide rods, the guide rods are fixedly connected with a square plate, the two guide rods located on the same side are sleeved with the same sliding plate, the sliding plates are slidably connected with the guide rods, and one side of each sliding plate is fixedly connected with a fixing base. According to the utility model, the multi-mode palm vein recognition terminal machine body can be effectively protected, the multi-mode palm vein recognition terminal machine body can be conveniently cleaned, dust is prevented from blocking a sensor, the quality of captured images is ensured to be clear, a user can clearly see displayed content, and the use experience of the user is ensured.

The invention relates to the technical field of palm vein recognition, in particular to an efficient palm vein representation attack detection method and system based on a lightweight neural network. The method comprises the following steps: acquiring an image to be identified; inputting a to-be-recognized image into the Xception entry stream, and performing preliminary feature extraction on the to-be-recognized image to obtain an initial feature; inputting the initial features into Block modules of a star network, and further mining high-order nonlinear features in the image by each Block module through convolution and star operation; and performing classification identification on the high-order nonlinear features to obtain classification result data. Real and forged palm vein images are distinguished by capturing vein texture features in the images, a single-channel near-infrared palm vein image is input, and a binary result of true and false classification is output.

1. The name of the design product: biometric identification device. 2. The use of the design product: can identify palm vein, palm print and other information, carry out identity recognition and information acquisition, and is used in the fields of payment and access control identity recognition. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The method comprises the steps that when it is detected that the palm of a user is placed on a door handle, a bottom palm vein recognition module is triggered to recognize palm center veins of the palm, and when the palm center veins pass verification, the bottom palm vein recognition module is triggered to recognize the palm center veins of the palm; a top palm vein recognition module is triggered to recognize hand back veins of a palm, and after verification of the hand back veins is passed, the intelligent door lock is controlled to be unlocked; or when it is detected that the palm of the user is placed on the door handle, the top palm vein recognition module is triggered to recognize the hand back vein of the palm, after the hand back vein verification is passed, the bottom palm vein recognition module is triggered to recognize the palm center vein of the palm, and after the palm center vein verification is passed, the intelligent door lock is controlled to be unlocked. According to the invention, through a palm double-sided vein recognition mechanism, unlocking is carried out only when the comparison of double characteristics is passed, so that the recognition safety of the intelligent door lock is improved.

1. The name of the design product: palm vein access control attendance machine. 2. The use of the design product: for palm vein recognition control access and attendance record. 3. The design points of the design product: in the perspective view. 4. The picture or photo that best indicates the design points: perspective view.

The utility model relates to an intelligent lock control device with double systems. The intelligent lock control device comprises a front plate information acquisition control system, a rear plate information acquisition control system and a control system, wherein the front plate information acquisition control system is used for acquiring unlocking information (fingerprints, passwords, human faces or palm veins) when a user is outside a door; the rear plate information acquisition control system is used for acquiring unlocking and locking information when a user is inside a door and wireless unlocking information (Bluetooth unlocking and WiFi unlocking) when the user is outside the door; the front plate information acquisition control system is used as a preferred system to judge and process the information and notify the rear plate control system to open a door according to a processing result; under normal conditions, the back plate information acquisition control system only serves as an acquisition control unit to feed back or execute instructions of the front plate system and does not make judgment. When the front board control system is damaged or out of order, the rear board control system serves as a standby system to be started, the judgment function of the front board is taken over, and a switch lock command is executed. Through the arrangement, the problem that the whole door lock cannot be opened and closed due to damage of the front plate or abnormal communication of the front plate and the rear plate can be solved.

A biometric recognition device for palm prints and palm veins includes: a light source being used to emit illuminating beams in a near-infrared band; a polarizing unit being used to convert the illuminating beams to polarized beams which are emitted to a palm; an imaging unit receiving retroreflected beams from a palm and being used to converge the retroreflected beams to a focal plane; a polarizing beam splitter forming transmitted beams and reflected beams based on different polarization directions of beams from the imaging unit, wherein the transmitted beams propagate after pass through the polarizing beam splitter, and the reflected beams propagate after reflected by the polarizing beam splitter. An identity authentication method is further provided. Based on the non-contact technical solution, only one light source is used to obtain the palm print images and palm vein images at the same time and in the same space.

The invention discloses a palm biological feature fusion recognition method and device, a storage medium and equipment, and belongs to the field of biological feature recognition. According to the method, the characteristic that the distribution positions of the palm print main line and the palm vein blood vessels are relatively fixed is utilized, the intersection points of the palm print main line and the palm vein blood vessels serve as the feature points, the feature point set used for registration and recognition is constructed, feature-level fusion of the palm prints and the palm veins is achieved, and comparison is conducted through the fused feature point set, so that the registration accuracy is improved. Therefore, palm biological feature fusion recognition is realized. According to the method, the advantages that the palmprint principal line for palmprint recognition is thicker, obvious and stable in feature and not prone to interference compared with other features, the blood vessel near-infrared imaging for palmprint vein recognition is hidden and not prone to interference, and the advantages of correlation and dependence of relative position distribution of palmprint and palmprint vein are combined, so that the accuracy of palmprint and palmprint vein recognition is improved, and the accuracy of palmprint and palmprint vein recognition is improved. The recognition precision and the recognition reliability of biological characteristics are greatly improved, and accurate recognition of palm prints and palm veins is achieved.

The invention discloses a palm vein recognition method and system based on multispectral imaging and deep learning and a storage medium, and relates to the field of biological feature recognition, and the method comprises the steps: obtaining the multi-dimensional information of a palm through the synchronous collection of near-infrared, short-wave infrared and visible light wave band images, and then carrying out the adaptive weighted fusion and enhancement processing of a multispectral image, thereby achieving the recognition of the palm vein. In the core recognition step, a double-branch deep learning model is adopted, one branch extracts static structure features from the vein feature map, the other branch analyzes blood flow dynamic features from a time sequence image, and finally the static structure features and the blood flow dynamic features are fused for integrated living body detection and identity recognition. Through three-dimensional vein texture modeling and a dynamic living body detection anti-counterfeiting mechanism, faking attacks of photos, silica gel molds and the like are completely eradicated, and the problems that in a complex environment, performance of a traditional method is reduced, faking attacks are likely to happen, and speed and precision are difficult to consider at the same time are effectively solved.

The utility model discloses a novel intelligent lock structure which comprises a lock body and a lock cylinder arranged in the lock body, a main control circuit board is arranged in the lock body, and an unlocking induction area is arranged on the lock body. The main control circuit board is provided with a processor, and a palm vein recognizer, a face recognizer, a fingerprint recognizer, a password input device, a magnetic card recognizer, a mobile phone APP remote unlocking networking module, a remote temporary password receiver, a mobile phone NFC unlocking communicator and a real-time monitor which are connected with the processor; the lock body is provided with a key unlocking channel connected with the lock cylinder and an alarm connected with the processor. The door lock is reasonable in structural arrangement, the functions of the door lock are diversified, one or more structures can be selected for unlocking operation according to needs, remote unlocking control can be achieved, unlocking convenience and effectiveness are improved, use safety of the door lock can be improved, use requirements of different conditions can be met, and the door lock is worthy of popularization and application. And the applicability and the practicability are high.

The application discloses a palm vein recognition method and device, equipment and storage medium, the method comprises: through the camera shoots palm image, and through the palm detection model of pre-training from palm image identifies first palm area;In the case where the target brightness value of the first palm area does not satisfy the preset brightness range, the exposure of the camera is adjusted according to the target brightness value, and the next palm image is shot by the camera;In the case where the target brightness value of the first palm area satisfies the preset brightness range, the second palm area is identified from the palm image;Palm vein features are extracted from the second palm area to determine the corresponding identity information according to the palm vein features. Through the above technical means, the palm area in the palm image is detected, the camera exposure is adjusted according to the brightness value of the palm area, so that the palm image with appropriate brightness is quickly collected for palm vein recognition, the recognition efficiency is improved, and the problem that the palm vein recognition in the prior art takes a long time is solved.

1. The name of the design product: palm vein identification terminal (HF-MP51). 2. The use of the design product: the design product is used for the identification of identity information. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: front view.

The invention discloses a method for recognizing palm prints and palm veins in a missing mode based on multi-level knowledge distillation, and provides three innovation points aiming at the problem that semantic information of different levels of a teacher network cannot be fully mined by a traditional distillation method: (1) knowledge distillation is carried out on three progressive levels of the teacher network, and in addition, the knowledge distillation is carried out on the three progressive levels of the teacher network; logic output is also subjected to fine-grained alignment, and it is ensured that the reasoning network fully learns overall semantic knowledge of the teacher network. (2) proposing a cross-modal feature distribution correlation distillation mechanism, and encouraging the reasoning network to learn representation correlation knowledge among modals instead of focusing on transmission of knowledge in a single modal; and (3) providing structural knowledge of distribution angles and distances of feature vectors of different samples in a high-dimensional space based on structural knowledge distillation among samples guided by classification boundaries, and meanwhile, enabling the inference network to pay more attention to the samples with classification uncertainty through a classification boundary guiding mechanism so as to refine the extracted structural knowledge.

The invention discloses a periscopic palm vein recognition method and an intelligent door lock, and the method comprises the steps: when a palm is placed in an area of a glass panel, a near-infrared light source emits infrared rays to the palm of a user; the reflecting mirror intensively guides infrared rays reflected by the palm to the imaging lens group; the imaging lens group optimizes the infrared light signal to obtain an optimized infrared light signal, and focuses the optimized infrared light signal to the image sensor; the image sensor converts the optimized infrared light signal into a digital image signal and sends the digital image signal to the control processing unit; and the control processing unit converts the digital image signal into a palm vein dark fringe image, extracts a palm vein feature value from the palm vein dark fringe image, and controls unlocking if the palm vein feature value exists in the memory. Through the periscopic optical structure design, ultra-short-distance high-quality palm vein imaging is achieved, a user does not need to adjust the hand position and distance deliberately, identity recognition can be completed only by naturally holding the door handle, operation is convenient, and the recognition success rate is high.

The invention discloses a multi-mode identity authentication method and device, electronic equipment and a storage medium, and the identity authentication method comprises the steps: combining face recognition and palm vein recognition at the same time, extracting a to-be-authenticated image at a time, and carrying out the ROI (region of interest) extraction through a target monitoring network, thereby obtaining a face image region and a palm vein image region; multiple image acquisition devices or time-sharing acquisition are not needed, so that the image acquisition efficiency and experience are improved; the face image area and the palm vein image area are respectively input into a pre-trained face recognition network and a pre-trained palm vein recognition network to obtain a face recognition result and a palm vein recognition result, and meanwhile, the face recognition result and the palm vein recognition result are considered to determine an identity authentication result, so that the limitation of single-mode authentication is overcome, and the authentication efficiency is improved. And the security and the accuracy of the identity authentication system are obviously improved.

This application provides a method and apparatus for generating palm vein sample data, which are used to improve the accuracy of obtaining palm vein sample data. The method includes: obtaining a feature matrix of a real palm vein image; wherein the feature matrix is ​​used to represent the distribution of image features of the real palm vein image; inputting the feature matrix into a generator in a pre-trained adversarial network to obtain palm vein sample data corresponding to the real palm vein image; wherein the dimension of the pooling matrix output by the pooling layer in the generator is the same as the dimension of the feature matrix.

This invention discloses a palm vein recognition method based on a domain adaptation strategy, relating to the fields of biometrics, identity authentication, and palm recognition. The method includes: Step 1: Constructing a dataset, including a source dataset and a target dataset. All data from the source dataset and a portion of the target dataset are used as the initial training set, and the remaining data from the target dataset are used as the initial test set. ROI extraction is then performed, and the initial training set is split into a formal training set, and the initial test set is split into a formal test set. Step 2: Designing an adversarial domain adaptation network. Step 3: Pre-training on the source dataset to enable the classification module to achieve a pre-defined classification performance on the source dataset. Step 4: Performing adversarial training on the formal training set to enable the palm feature extraction module to learn domain-invariant features. Training ends when the preset number of iterations or model threshold is reached. Step 5: Testing using the formal test set.

The present invention relates to a miniaturized vein recognition module and a B-pillar trim panel integrated with same. The miniaturized vein recognition module comprises a camera module, a controller module and a micro switch module. The controller module comprises a PCBA, an MCU component, an SOC component and an RGB lamp being integrated onto the PCBA. The micro switch module is pressed by a hand to wake up and power on the vein recognition module. The camera module recognizes and collects palm vein feature information and transmits same to the controller module. The SOC component inside the controller module executes vein extraction, enhancement and feature comparison and the MCU component transmits a vein recognition result to an in-vehicle infotainment system, so that an in-vehicle infotainment center console gives feedback to lock and unlock vehicle doors. The present invention eliminates an original module housing structure, so that a required trim panel interior space size is relatively small, thereby better satisfying requirements for miniaturized B-pillar trim panels of framed and frameless vehicle doors.