201 results about "Palm vein" patented technology

The invention provides a palm vein feature extraction and depth adaptive matching method, which relates to the technical field of biological recognition, and is characterized in that a three-dimensional space-time convolutional neural network and a graph neural network are combined to extract space depth features, time blood flow dynamic features and blood vessel topological features of palm veins, and information bottleneck is utilized to optimize and screen a feature subset with the most distinguishing property; the method breaks through the limitation of traditional two-dimensional texture feature extraction, obtains abundant palm vein information through multi-source data acquisition (such as near-infrared imaging, a light field camera and a spectrum sensor), overcomes the interference of environmental factors such as illumination, angle and noise, achieves the high-precision modeling and stable extraction effects of palm vein features, and can achieve the real-time extraction of the palm vein features in a complex scene. A high recognition rate is kept under different illumination conditions or user hand posture changes, the robustness of the palm vein recognition system is remarkably improved, and the method is suitable for scenes with high safety requirements.

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 subway track automatic ticket checking method and an automatic ticket checking device thereof. The method specifically comprises the following steps: S1, feature acquisition; s2, performing feature fusion; s3, performing anomaly detection; the invention relates to the technical field of subway track automatic ticket checking. According to the subway track automatic ticket checking method and the automatic ticket checking device thereof, through cooperation of palm vein and gait recognition and a non-contact collection mode, the safety of a ticket checking system is improved, the false recognition rate is reduced, meanwhile, the accuracy of abnormal behavior detection is effectively improved, the self-learning ability is provided through a reinforcement learning algorithm, and the accuracy of ticket checking is improved. The ticket checking strategy can be automatically adjusted according to the real-time passenger flow and the operation state, optimal configuration multi-mode identification of resources is achieved, efficiency and safety are balanced, the crowding degree in the peak period can be reduced, and high-satisfaction travel experience is provided for passengers.

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 relates to an access control attendance system and an access control attendance method based on biological recognition, which solve the problems of accuracy and reliability of identity recognition through a palm print and palm vein recognition technology and effectively avoid potential safety hazards caused by identity misjudgment. And the national secret CPU unit prevents data transmission from being stolen or tampered in a data transmission process through dynamic password authentication and an encrypted communication protocol, so that the safety of the access control system is guaranteed. In terms of key management, a key updating mechanism solves the problem of security risks caused by increase of the number of times of use of the key, and security threats caused by key leakage are prevented. The key backup mechanism ensures the recoverability of the key, and avoids the situation that the system cannot operate normally due to the loss or damage of the key. According to the access control system, transaction records are stored and managed through the block chain technology, the problems of data integrity, traceability and high efficiency in the management process of the access control system are solved, automatic management is achieved, and the safety and stability of the whole access control system are improved.

The invention relates to the technical field of biological feature image recognition, and discloses an image fusion recognition method and device based on an ultra-light network, a medium and equipment, and the method comprises the steps: carrying out the feature extraction of an input multi-modal image through employing an improved Co3Net network module, and obtaining a multi-scale feature; carrying out feature extraction on the input multi-scale features by adopting an ultra-light network module to obtain global features; a self-attention mechanism module is adopted to obtain a self-attention map according to the input global features; performing forward propagation on the self-attention map by adopting a forward propagation unit to obtain final forward propagation output; and adopting a classifier to obtain a multi-modal image classification label according to the final forward propagation output. By applying the method, the calculation complexity and parameter quantity of the palm print and palm vein recognition method can be reduced, and the problem that the method is difficult to operate efficiently on embedded equipment and mobile terminals with limited resources is solved.

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 highly convenient and safe palm lock, which comprises a lock body, a palm print acquisition module, a palm vein acquisition module, an iris recognition module, a control unit and a mechanical execution unit, and is characterized in that the palm print acquisition module is used for acquiring palm surface texture features and comprises an optical imaging unit and an infrared light supplement lamp; the palm vein acquisition module is used for acquiring vein blood vessel distribution characteristics in a palm and comprises a near-infrared light source and an infrared sensor; the iris recognition module is used for obtaining human eye iris texture features. Therefore, single-feature quick unlocking (convenience) and multi-feature combined verification (high safety) are supported, a user can dynamically configure a verification mode through a mobile phone APP to adapt to different scene requirements (such as household daily palmprint and office palmprint + iris), the three biological features are independently collected, the forging difficulty is high, the combined verification safety is high, and the verification efficiency is high. The palm fitting area is designed to be arc-shaped and aligned with the camera, and the identification time is less than or equal to 2 seconds.

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 relates to the technical field of image fusion and classification, and discloses a multi-modal image fusion recognition method and device, a medium and equipment. The method comprises the following steps: acquiring different types of multi-modal images; performing shallow feature extraction on the multi-modal image by using a convolutional layer, and performing feature fusion to obtain shallow fusion features; performing global feature extraction on the shallow fusion features to obtain global features; performing feature fusion on the shallow fusion features and the global features to obtain deep fusion features; performing local feature extraction on the deep fusion features to obtain local features; and performing full connection processing on the local features, and identifying to obtain a category label of the multi-modal image. By applying the method, fusion identification of the palm print and palm vein multi-mode images can be realized, and the identification accuracy and the equal error rate on a public data set and an incomplete data set are improved.

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.

1. Name of the Design Product: Palm Vein Recognition Device. 2. Use of the Design Product: Used for collecting palm vein images and realizing identity recognition and authentication. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective View.

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 non-contact palm vein recognition intelligent security and protection management terminal, which comprises a security and protection module and at least two identity verification modules including a palm vein verification module. The non-contact palm vein recognition intelligent security management terminal further comprises a hierarchical authorization module which is connected with the security module and the palm vein verification module, and the hierarchical authorization module is used for granting a set permission to a security application module when verification of the palm vein verification module is passed. The palm vein verification module is a non-contact palm vein verification module; according to the utility model, the security of the security management terminal can be improved, and the accuracy and efficiency of identity verification of the security management terminal can be improved at the same time.

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 utility model relates to the technical field of cash registers, and discloses a small-size multifunctional cash register which comprises a main body, a front screen arranged in the main body, a rear screen arranged in the main body, a palm vein camera arranged in the main body, and a code scanning camera arranged in the main body. And a card swiping sensing module is arranged in the main body. According to the small-size multifunctional cash register, the position of the front screen is adjusted, the camera and the sensing module are arranged on one side of the front screen, the effect that the camera and the sensing module can be contained without enlarging the size of the cash register is achieved, a clamping groove is formed in the main body, components are clamped in the main body, and the clamping groove is formed in the main body; the interior of the main body is more compact and higher in stability, heat dissipation can be achieved by using small heat dissipation fins by using the processor with smaller heat productivity after upgrading, and the problem that the size of the cash register is increased due to the fact that the cash register uses the processor with higher performance and is provided with a plurality of cameras and sensing modules is solved.

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.

The invention discloses a palm vein feature recognition method and device, and the method comprises the steps: obtaining a palm vein image, extracting a target contour of a palm object in the palm vein image, recognizing key feature points in the target contour, and enabling the key feature points to be used for positioning the edge of a palm vein region, therefore, the palm vein region in the palm vein image can be quickly positioned based on the key feature points used for positioning the edge of the palm vein region, and after the palm vein region is determined, the palm vein feature recognition can be performed in the region including the palm vein feature, so that the recognition accuracy is improved. The efficiency and accuracy of identifying the palm vein features in the palm vein image are effectively 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 present invention discloses a hand cleaning method, an intelligent lock and a storage medium, which relate to the technical field of computer vision. In this method, when it is detected that a user enters the recognition range, the identity of the user is recognized according to the palm vein characteristics of the user. When the recognition is passed, the bacterial content of the user's palm is detected to obtain the bacterial content; a palm image of the user is acquired, and dirt in the palm image is recognized to obtain the dirt content in the palm image; a bacterial dirt detection result is generated by combining the bacterial content and the dirt content. If the bacterial dirt detection result meets a preset condition, the user's hand is cleaned. Thus, the problem that the current intelligent lock cannot automatically clean the user's hand is solved.

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.