303 results about "Palm print" patented technology

Embodiments of this application disclose a palm print recognition method, a method for training a feature extraction model, a device, and a storage medium, and belong to the field of computer technologies. The method includes: obtaining sample hand images for each of a plurality of sample user identifiers, at least two of the sample hand images associated with the each of the plurality of sample user identifier being acquired from image acquisition devices having different image acquisition characteristics; calling a feature extraction model to extract sample palm print features of the plurality of sample user identifiers; and adjusting the feature extraction model according to the sample palm print features of the plurality of sample user identifiers.

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.

Disclosed in the present invention are a palmprint recognition method and apparatus, which relate to the technical field of palmprint recognition authentication, and solve the problem of low palmprint recognition accuracy caused by incapability of correcting palm postures in the prior art. The palmprint recognition method comprises: acquiring a palm image to be recognized; performing key point detection on the palm image on the basis of a preset first key point detection method, so as to obtain a first palm key point set; constructing an affine transformation matrix on the basis of the first palm key point set; on the basis of the first palm key point set and the affine transformation matrix, performing alignment and extraction on the palm image by means of a preset alignment and extraction method, so as to obtain first palmprint information, the first palmprint information comprising a palmprint attention region and a palm key point set; and inputting the first palmprint information and pre-stored second palmprint information into a preset first recognition model, and outputting a recognition result. The present invention can align palm images and correct palm postures, and can refine the granularity of palmprint features, thus improving the precision and accuracy of palmprint recognition.

The invention discloses a palm image recognition method and system based on fusion of a convolutional neural network and an image neural network, and belongs to the technical field of crossing of biological feature recognition and artificial intelligence. The problems that in the prior art, local texture features and a global topological relation are separated, dynamic scene adaptability is poor, and multi-modal features are not fully fused are solved. The method comprises the following steps: 1) designing a dual-path hybrid network architecture (DPHGCN), extracting multi-scale local texture features through a lightweight CNN, constructing an adaptive graph structure by using a dynamic graph generator (DGG), and modeling a topological relation between key points in combination with a graph attention network; 2) developing a cross-modal feature alignment module (GIPL), and realizing dimension mapping and semantic alignment of CNN feature graph and graph node embedding; and 3) providing a layered attention fusion mechanism, and combining space attention, topological attention and cross-modal attention to dynamically weight and fuse heterogeneous features. According to the method, 98.7% of recognition accuracy is achieved on a public data set, the reasoning speed reaches 32 ms / frame, 76.2% of robustness is still kept in a 50% shielding scene, and the method can be widely applied to the fields of financial payment identity authentication, medical palmprint pathological analysis, real-time security verification and the like.

The invention relates to the technical field of palmprint recognition, and discloses a palmprint recognition method based on multi-feature fusion, and the method comprises the steps: collecting a palmprint image, carrying out the preprocessing of the palmprint image, and generating a palmprint ROI image; extracting a multi-scale feature vector of the palmprint ROI image and a histogram feature vector of the palmprint image; splicing the multi-scale feature vector and the histogram feature vector to generate a target feature vector; and calculating the similarity between the target feature vector and the feature vector of the palmprint in a preset database, and outputting a palmprint recognition result according to the similarity. A hierarchical multi-scale filtering mechanism is utilized to process features of different scales layer by layer, and rich texture features are effectively captured; the importance of different scale features is dynamically adjusted by using a multi-scale channel attention mechanism, so that the network pays more attention to feature information beneficial to a recognition task; the histogram features are extracted and fused with the multi-scale features, so that the recognition result is more effective, the accuracy of the recognition result is improved, and the robustness of the recognition result is enhanced.

The invention provides a low-carbon intelligent safety roof system based on multi-information interaction, and the system comprises a roof integrated control module which achieves the integrated control based on an automobile roof structure. The layered structure is adopted, structures with different functions are mounted and integrated in a layered mode, the multifunctionality of an automobile roof is improved, sunroof palm print / gesture unlocking is adopted, a sunroof can be rapidly opened for escape in an emergency, CO2 / smoke concentration, temperature and infrared thermal imaging recognition in an automobile and accumulated water liquid level height are monitored in real time, and the safety of the automobile is improved. Through opening and closing of a roof skylight, an in-vehicle air conditioner, in-vehicle spraying and air exhaust ventilation, various problems of harming life and property safety during running or stopping of various automobiles are effectively avoided, and roof integrated control independent of an automobile console and low-carbon and environment-friendly manufacturing materials are adopted. Intellectualization, safety and low carbon of the roof are achieved through multi-information interaction, the limitation of single function design of a traditional roof system is broken through, and multifunctionality of the automobile roof is achieved.

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 palmprint recognition method and device, a storage medium and electronic equipment, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining at least one palm image sample of each known user in a plurality of known users; determining a palmprint area image and a first palmprint curve image of the corresponding palmprint according to each palm image sample; according to the palmprint area image and the first palmprint curve image, a feature extraction model is trained to obtain a palmprint recognition model, the feature extraction model comprises a first feature extraction module and a second feature extraction module which have the same network structure and share parameters, the first feature extraction module is used for extracting features of the palmprint area image, and the second feature extraction module is used for extracting features of the palmprint curve image; the second feature extraction module is used for extracting features of the first palmprint curve image; palmprint recognition is performed based on the palmprint recognition model, so that data enhancement training of the palmprint recognition model can be realized, and the palmprint recognition accuracy of the model is improved.

The utility model relates to the technical field of intelligent locks, and discloses an intelligent lock handle and an intelligent lock, the intelligent lock handle is suitable for driving the intelligent lock to open, and the intelligent lock handle comprises a holding rod for a user to hold a sliding door; the palmprint recognition sensor is embedded in the circumferential surface of the holding rod and is suitable for acquiring palmprint information of a user and generating control information according to a comparison result of the acquired palmprint information of the user and preset palmprint information; the controller is electrically connected with the palmprint recognition sensor and suitable for controlling the intelligent lock according to control information generated by the palmprint recognition sensor, a user can directly open a door after holding the holding rod with the hand to complete palmprint recognition, recognition action and door opening action are combined into a whole, and the whole door opening process is simplified.

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 non-contact palmprint collection method, device and equipment and a storage medium, and relates to the field of palmprint payment. Acquiring and extracting a palmprint image according to the image acquisition instruction, identifying pixel information of the palmprint image, and determining the number of palm key points and pixel coordinate data of the palm key points; the palm key points are finger tip points of fingers and wrist bottom end points close to the bottom end of a thumb; calculating the integrity of the palm image and the palm position according to the number of the palm key points and the pixel coordinate values; and when the recognized palm image is incomplete, calculating adjustment parameters of an aperture gear in the non-contact palmprint acquisition device according to the palm position, and adjusting the focal length and depth of field of an aperture to obtain a complete palmprint image. The method can actively detect the integrity and position of the palm image, and adaptively adjust the aperture size according to the integrity and position of the palm image to complete image acquisition work.

A sample palm print image generation method includes generating a major palm print line according to a preset major palm print line distribution pattern, determining a palm print distribution region in which the major palm print line is located, generating a minor palm print line in the palm print distribution region based on a preset minor palm print line distribution pattern, generating a candidate palm print image based at least on the major palm print line and the minor palm print line, labeling the candidate palm print image with a candidate image label that represents a palm print distribution of the candidate palm print image including at least one of a major palm print line distribution or a minor palm print line distribution, and inputting the candidate palm print image to a palm print generation model to obtain a sample palm print image.

1. Name of the Design Product: Dynamic Graphical User Interface for Identifying the Status of an Electronic Device. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the graphical user interface. 4. Picture or Photograph that Best Illustrates the Design Key Points: The front view of Design 1. 5. For planar products, other views are omitted. 6. Use of the Graphical User Interface: Used to dynamically display the identification status of the device so that users can understand that the device is currently identifying the input biometric features (such as face, fingerprint or palmprint). 7. Human-Computer Interaction Method of the Graphical User Interface: Users can click on the controls in the interface or slide to perform operations. For example, click on the "Password Unlock" control or the "AI Recognition" control in the front view. 8. Design 1 is designated as the basic design.

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 relates to a palm image acquisition method, system and device. The method comprises the following steps: acquiring the distance between a palm and palm image acquisition equipment; under the condition that the distance is within a preset range, acquiring the light intensity of the environment where the palm image acquisition equipment is located; according to the distance and the light intensity, setting a brightness value of the light supplement lamp; indicating palm image acquisition equipment to acquire an image of the palm; calculating the image quality of the image; and outputting the image under the condition that the image quality meets a preset image quality standard value. According to the scheme, on one hand, the mode of adjusting the brightness of the light supplementing lamp is adopted, and compared with adjustment of an image sensor, the adjustment speed is high, control links are few, and logic is simple; and on the other hand, when the palm print image and / or the palm vein image of the palm are / is collected, environmental factors such as the palm distance, infrared light and visible light are considered, the problems of too dark or too bright images, noise and the like can be effectively avoided, and the image quality is improved.

The invention discloses a three-dimensional palm print region-of-interest extraction method based on a CMV3-TFNet lightweight model. The method comprises the following steps: firstly, acquiring twelve deformed fringe patterns through a four-step phase shift method by using a three-dimensional palm print acquisition system, and generating a two-dimensional palm print depth map; preprocessing the collected palm image data; a CMV3-TFNet model architecture is used to carry out palm print center positioning and rotation angle prediction on a preprocessed image, a polar coordinate transformation network PTN is introduced into a model structure to enhance rotation robustness, depth separable convolution and a lightweight Transform module are used to improve calculation efficiency, and a CBAM module is used to enhance feature extraction capability. And finally, cutting the palmprint image after rotation correction, and extracting a region of interest of the palmprint. According to the method, good performance is obtained on various data sets, and the capability of accurately extracting the palmprint region of interest in a rotating complex scene is achieved.

The invention discloses an all-in-one collection device, and relates to the technical field of human body biological characteristic collection. A closed top cover is rotationally arranged above the collector body, and a display screen is fixedly arranged in the center of the inner side of the front surface of the closed top cover; a touch screen is hinged to the right side of the collector body, and a self-cleaning mechanism corresponding to the touch screen is arranged in the right side of the collector body; wherein a dust removal mechanism is arranged in the top surface of the collector body, and the dust removal mechanism comprises a dust removal brush rod. According to the all-in-one collection equipment, the collector body and the closed top cover are opened to drive the dust removal brush rod to clean the certificate collector, the voiceprint collector and the palm print collector, and meanwhile, after the abutting sliding block pressed and covered by the closed top cover ascends, the self-cleaning sliding rod is driven to remove dust on the touch screen; and the influence of impurities such as palmprint of a collector on subsequent collection is avoided.

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 an SMA-ConvNeXt model-based application method in palmprint information personnel identity recognition, which comprises the following steps of: making an SMPDRED infrared integrated palmprint information recognition benchmark test set, preprocessing a data set, and dividing the data set into a training set and a verification set; building a ConvNeXt basic model, initializing model parameters, and training the basic model by adopting a training set; the ConvNeXt basic model is improved, and an improved SMA-ConvNeXt model is obtained; the improved SMA-ConvNeXt model is verified, and model indexes of the improved SMA-ConvNeXt model and a ConvNeXt basic model are compared; and according to the improved SMA-ConvNeXt model, personnel identity recognition is carried out through palm print information.

The present application discloses an image processing method and apparatus, a device, and a medium. The method comprises: acquiring a first sample image comprising a plurality of regional images; calling an attention mechanism network to identify a first regional image and a second regional image with different attention degrees among the plurality of regional images on the basis of an attention parameter; adding a first predicted label to the first regional image, and adding a second predicted label to the second regional image, wherein the first predicted label and the second predicted label are used for indicating different image definition; acquiring a first reference label of the first regional image and a second reference label of the second regional image; and correcting the attention parameter on the basis of the difference between the first predicted label and the first reference label and the difference between the second predicted label and the second reference label, wherein the corrected attention parameter is used for extracting an embedded feature for classification of a palmprint image. Use of the present application can improve the accuracy of extraction of embedded features of palmprint images and the accuracy of classification of palmprint images.

Embodiments of the present application provide a palmprint image generation method and apparatus, a device, a storage medium, and a program product. The method comprises: determining a plurality of control points on the basis of a predetermined palmprint curve template, and generating a Bézier curve on the basis of the plurality of control points; on the basis of the Bézier curve, generating a palm crease energy map having crease information, the palm crease energy map comprising a palm crease having a crease distribution same as that of the Bézier curve and a crease type different from that of the Bézier curve, wherein the crease type is used for describing the crease information and corresponds to crease direction energy of each pixel on the palm crease; and on the basis of the palm crease energy map, generating a photorealistic palmprint image having detailed texture information.

The present invention discloses a financial payment security verification device and method based on RFID technology, the device comprising a main control unit, a tunable antenna array, a biometric feature collection unit, a hardware encryption storage unit and a blockchain processing unit; the main control unit respectively performs RFID communication control and biometric feature recognition through a dual processor architecture; the tunable antenna array adopts dynamic spectrum hopping technology to switch between multiple frequency bands; the biometric feature collection unit integrates fingerprint and palm print recognition functions; a dual random source is used to generate a frequency hopping sequence, frequency band switching is performed in combination with channel quality assessment, and transactions are verified through blockchain technology; the present invention significantly improves the security and reliability of the payment process through the collaboration of multiple biometric feature recognition, dynamic spectrum management and blockchain technology, and effectively prevents various payment frauds and attacks.

The present invention relates to an artificial intelligence Four Pillars of Destiny (Bazi) fortune-telling apparatus and method, a recording medium containing a computer program capable of executing this method, an artificial intelligence Four Pillars of Destiny fortune-telling service system and method using the same, more specifically, to an apparatus and method that improves the accuracy of Four Pillars of Destiny fortune-telling by utilizing artificial intelligence (AI) machine learning, deep learning, and large language models (LLM) to analyze past Four Pillars of Destiny readings, thereby enabling users to improve their understanding of their own Four Pillars of Destiny fortune-telling, a recording medium containing a computer program, and a service system and method using the same. An artificial intelligence-based Four Pillars of Destiny (Bazi) analysis device according to an embodiment of the present invention is characterized in that, in response to input Four Pillars of Destiny information, a computing device including at least a processor outputs Four Pillars of Destiny analysis data through Four Pillars of Destiny analysis and analysis, and in order to improve the accuracy of Four Pillars of Destiny analysis, the data analyzed based on answers (question and answer) obtained through a multi-stage questioning process, is comprehensively analyzed (descriptive statistics and inference statistics) by combining physiognomy (image), palm lines - palmistry (image), voice / voice physiognomy (voice) data and the Four Pillars of Destiny of people with similar Four Pillars of Destiny, and then utilizes artificial intelligence (machine learning and deep learning, etc.).

Certain biometric authentication and verification methods use multiple biometric modalities of a given user in order to enhance their security. Circumvention of such methods sometimes involves presenting multiple biometrics as if they come from a single user when, in fact, they arise from more than one person. To thwart such attacks, it is necessary to bind multiple biometric modalities to each other. One binding method captures physiological signals of a user, such as pulse rate, in conjunction with the capture of each biometric. When the physiological signals associated with each of the biometric modalities match, the biometrics are bound. Another binding method captures an intermediate biometric when capturing each of the multiple biometric modalities. When the intermediate biometrics match, the multiple biometric modalities are bound. For example, for face and finger biometrics, a palm intermediate biometric may be used.

The utility model discloses a palmprint recognition device with pressure induction, comprising a first substrate layer which is a flexible substrate; the palmprint recognition layer is arranged on the upper surface of the first substrate layer; the first pressure sensing layer is arranged on the lower surface of the first substrate layer; a second substrate layer; the second pressure sensing layer is arranged on the upper surface of the second substrate layer; and the flexible isolation layer is arranged between the first pressure sensing layer and the second pressure sensing layer, so that the first pressure sensing layer and the second pressure sensing layer jointly form a pressure sensing capacitor. The palmprint recognition device can improve the safety of palmprint recognition.

1. Name of the design product: Palmprint recognition device. 2. Use of the design product: It can identify human palm information, perform identity recognition and information acquisition, and is used in fields such as payment and access control identity recognition. 3. Design key points of the design product: lies in the shape. 4. Picture or photo that best shows the design key points: Stereogram 1.

1. The name of the design product: the terminal device of the ladder control. 2. The use of the design product: used in the elevator, identify face, palm print, two-dimensional code, IC card access permission and display relevant state information. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: front view.

The embodiment of the disclosure provides a controllable palmprint sample generation method based on a stochastic differential equation; it relates to the field of computer vision. The method comprises constructing an unconditional diffusion model network architecture and defining a forward process and a reverse process; training an unconditional diffusion model according to the forward process and a palmprint image to obtain a noise image; according to the reverse process, the trained unconditional diffusion model is used for sampling the noise image to generate an unconditional palmprint sample; a conditional encoder is introduced into the trained unconditional diffusion model; the weights of the unconditional diffusion model are frozen, and the conditional encoder is trained according to a conditional image; when the conditional encoder loss function converges, the frozen weights are unlocked, and the unconditional diffusion model introduced with the conditional encoder is trained according to the forward process, the palmprint image and the conditional image; according to the reverse process, the trained unconditional diffusion model with the conditional encoder is used for sampling to generate a conditional palmprint sample. Thus, the palmprint sample authenticity, diversity and controllability are improved.