480 results about "Fingerprint image" patented technology

The invention relates to the technical field of identity authentication, and discloses an identity authentication method and system based on a large-model multi-mode venue, and the method comprises the steps: obtaining a fingerprint image, a face image and behavior interaction data of a user; performing fingerprint feature extraction according to the fingerprint image to obtain a fingerprint feature vector; performing facial feature extraction according to the facial image to obtain a facial feature vector; performing feature fusion on the fingerprint feature vector and the face feature vector, and generating a fusion feature vector after data dimension reduction; performing behavior feature extraction according to the behavior interaction data to obtain a behavior feature vector; and after cross-modal mapping is carried out according to the fusion feature vector and the behavior feature vector, matching degree analysis is carried out in a preset unified semantic space, and an identity authentication result is obtained. The method has the following effect that the identification security of identity authentication can be improved.

The invention relates to the technical field of fingerprint identification, and discloses a small-area fingerprint identification method and device, and a medium. The method comprises the following steps: acquiring a to-be-identified fingerprint image; performing ridge line feature point extraction on the to-be-identified fingerprint image to obtain a feature point set; the feature point set comprises a first feature point and / or a second feature point, the first feature point is a bifurcation point and / or an end point of a fingerprint ridge line, and the second feature point is a maximum curvature point of the fingerprint ridge line; based on feature points in the feature point set, performing image feature extraction on the to-be-identified fingerprint image to obtain a plurality of corresponding local fingerprint image features; and performing feature matching on the local fingerprint image features and preset target fingerprint image features, and generating a fingerprint identification result of the to-be-identified fingerprint image based on a feature matching result. According to the embodiment of the invention, the recognition precision of small-area fingerprint recognition can be improved.

A fingerprint sensing system. The fingerprint sensing system includes: at least one sensor; at least one display device; at least one application processor; and at least one secure enclave processor. The application processor(s) receives fingerprint data from the sensor(s) and provides the fingerprint data to the secure enclave processor(s). The secure enclave processor(s) decodes the fingerprint data and provides a signal indicative of at least one matched node. The application processor(s), responsive to receipt of the signal indicative of the matched node(s), presents at least a portion of a synthetic fingerprint image via at least one display device corresponding to the matched node(s).

The invention provides an engineering machinery man-machine binding card punching management system based on fingerprint biological recognition, and belongs to the field of fingerprint recognition, and the method comprises the steps: obtaining a fingerprint image of a to-be-recognized constructor and an initial fingerprint matching degree; dividing the fingerprint image of the construction personnel to be identified into a plurality of areas; obtaining a normal fingerprint area and a suspected stain area according to the shape of each area; according to the number of normal fingerprint areas around each suspected dirty area and the distribution of pixel points in each suspected dirty area, obtaining a dirty area; and completing identification according to the normal fingerprint area, the stain area and the initial fingerprint matching degree of the to-be-identified constructor. The invention aims to solve the problem that the construction personnel cannot identify the fingerprint when performing fingerprint card punching identification on the construction site due to the fact that the fingers of the constructor may be stained with oil stains, soil and the like in the construction process. And the problem that fingerprint card punching comparison is wrong may occur.

A fingerprint recognition method and apparatus are provided. The method is applied to the field of fingerprint recognition technologies. The method includes: when a first fingerprint image is successfully matched, judging whether matching information of the first fingerprint image satisfies a first preset condition; when the matching information of the first fingerprint image satisfies the first preset condition, judging whether a cumulative update count of a second fingerprint template is greater than a first quantity threshold; when the cumulative update count of the second fingerprint template is greater than the first quantity threshold, judging whether priority information of the second fingerprint template satisfies a second preset condition; and when the priority information of the second fingerprint template satisfies the second preset condition, updating the second fingerprint template with the first fingerprint image. A high-quality fingerprint image is selected through a plurality of judgment conditions to update a fingerprint template library. Matching is performed based on an updated fingerprint template library. This can increase a fingerprint unlocking rate and an unlocking speed, so that user experience in unlocking is improved.

A fingerprint recognition method and apparatus are provided. The method is applied to the field of fingerprint recognition technologies. The method includes: when a first fingerprint image is successfully matched, judging whether matching information of the first fingerprint image satisfies a first preset condition; when the matching information of the first fingerprint image satisfies the first preset condition, judging whether a cumulative update count of a second fingerprint template is greater than a first quantity threshold; when the cumulative update count of the second fingerprint template is greater than the first quantity threshold, judging whether priority information of the second fingerprint template satisfies a second preset condition; and when the priority information of the second fingerprint template satisfies the second preset condition, updating the second fingerprint template with the first fingerprint image. A high-quality fingerprint image is selected through a plurality of judgment conditions to update a fingerprint template library. Matching is performed based on an updated fingerprint template library. This can increase a fingerprint unlocking rate and an unlocking speed, so that user experience in unlocking is improved.

The application belongs to the technical field of two-dimensional material devices, and discloses a strain-enhanced multi-sensory fusion optoelectronic synapse device and a preparation method and application thereof. The optoelectronic synapse device comprises, from bottom to top, an insulating layer, an electrode and a functional layer. The material of the functional layer is an in-plane polarization two-dimensional ferroelectric material, and the functional layer is applied with a tensile strain along the direction of its own polar axis. The optoelectronic synapse device simulates the perception of visual information through the photoconductivity effect of the functional layer and simulates the sense of touch through the tensile strain. The synergistic effect of the photoconductivity effect and the tensile strain significantly enhances the persistent photoconductivity (PPC) of the synapse device, and the enhancement processing of a fingerprint image is realized through the characteristics of the device. At the same time, by applying the tensile strain along a specific direction, the in-plane polarization intensity is significantly improved, the constraint of the depolarization field is avoided, the stability and performance of the ferroelectric device are improved, and a new way for improving the performance of the optoelectronic synapse device is provided.

A fingerprint information processing apparatus includes: an output unit that outputs a certainty factor that is an index indicating probability in which fingerprints indicated by a fingerprint image correspond to at least one of a plurality of pattern types, by using the fingerprint image and a learning model constructed by machine learning using learning data including a sample image indicating fingerprints; and a processing unit that performs processing based on the certainty factor.

Disclosed is a method for providing a trained machine learning system for classifying images of fingerprints, the method comprising: providing a training data set, comprising sets of images of fingerprints, wherein each image in the training data set further comprises a binary label encoding a class designation; using the training data set to train a machine learning system, wherein the machine learning system comprises a classical machine learning model implemented on a classical processor and a quantum machine learning model implemented on a quantum processing unit (QPU); encoding the image data of each image in the training data set into a feature vector using the classical machine learning model, obtaining a family of feature vectors; using the family of feature vectors obtained from the images in the training data set to train the classical machine learning model and the quantum machine learning model, for providing the trained machine learning system, comprising a trained classical machine learning model and a trained quantum machine learning model.

The invention discloses an ultrasonic fingerprint identification method, a chip, a system, electronic equipment and a storage medium, and the method comprises the steps: controlling an ultrasonic piezoelectric transducer to work based on first configuration information, and obtaining one or more first fingerprint images; performing spatial frequency identification on the one or more first fingerprint images, and determining a first spatial frequency corresponding to the one or more first fingerprint images; when one or more first fingerprint images meet the adaptive condition, determining second configuration information based on the first spatial frequency and the first configuration information; and updating the first configuration information by adopting the second configuration information. According to the method, the first configuration information is dynamically adjusted based on the first spatial frequency corresponding to the first fingerprint image, so that the adjusted second configuration information can effectively guarantee the overall performance of the ultrasonic fingerprint system, the quality of the fingerprint image acquired subsequently is improved, the recognition effect is improved, and the universality of the system is improved.

An apparatus may include a display stack, an ultrasonic sensor stack and a high-impedance stack including one or more high-impedance layers. The ultrasonic sensor stack may include an ultrasonic transceiver layer and an ultrasonic transceiver circuitry (UTC) layer. Each of the high-impedance layers may have an acoustic impedance that is higher than an acoustic impedance of the UTC layer. The high-impedance stack and the ultrasonic sensor stack may form an acoustic resonator bounded by the UTC layer and the high-impedance stack. The acoustic resonator may be configured to enhance the ultrasonic waves transmitted by the ultrasonic sensor stack at a peak frequency of the ultrasonic sensor stack. The peak frequency may be a frequency used by the ultrasonic sensor stack for obtaining fingerprint images. An apparatus stack portion that includes the ultrasonic sensor stack may have a thickness corresponding to a multiple of a quarter wavelength at the peak frequency.

The invention provides a body fat scale and a control method thereof, the body fat scale comprises an identity recognition unit and a control unit, the identity recognition unit comprises a first fingerprint recognition module and a second fingerprint recognition module, and the first fingerprint recognition module comprises an angle-adjustable first fingerprint recognition panel; the second fingerprint identification module comprises a second fingerprint identification panel which forms a first inclination angle with the vertical plane, and the angle of each module accords with the natural angle of contact between the finger of the user and the body fat scale, thereby improving the user experience. The control unit is configured to determine a target fingerprint identification module adapted to a user; determining a first identifier corresponding to the first fingerprint image in response to a fingerprint input operation of a user through a target fingerprint identification module; and based on the first identifier, establishing a matching relationship between the first initial data and the basic data corresponding to the first identifier. In the use process of the user, the use habits and the like of the user are learned to adapt to operation characteristics of different users, and the user identity is quickly and accurately determined.

The invention discloses a fingerprint verification method and device based on double-finger fingerprint image acquisition, and relates to the technical field of biological recognition, and the method comprises the following steps: deploying a light sensor, carrying out the primary adjustment of an illumination light source, collecting the light intensity data of the fingerprint surface when two fingers press through the light sensor, and obtaining a fingerprint verification result according to the light intensity data; the method comprises the following steps: deploying a light sensor, constructing a closed-loop adjustment mechanism of primary adjustment, secondary adjustment and quality feedback, acquiring light intensity data of the fingerprint surfaces of the two fingers in real time, judging an abnormal condition, performing secondary adjustment on an illumination light source, acquiring fingerprint images of the two fingers, and judging whether the quality of the fingerprint images reaches the standard or not. According to the two-finger fingerprint verification method, the abnormity of insufficient light source intensity or improper angle is pointedly corrected through secondary adjustment, and the image quality evaluation standard combining the edge sharpness and the effective area proportion provides reliable data support for two-finger fingerprint verification, and the fingerprint verification accuracy and the actual application effect are improved.

The embodiment of the invention provides a fingerprint identification method and device, electronic equipment and a storage medium. The fingerprint identification method comprises the following steps: acquiring a to-be-identified fingerprint image; the to-be-identified fingerprint image is identified through a plurality of seed templates, the plurality of seed templates are spliced into at least one first virtual puzzle, and the adjacent seed templates in the first virtual puzzle have overlapping areas; if the identification of the fingerprint image to be identified by the plurality of seed templates fails, determining at least one reference area from the first virtual puzzle according to the similarity between the fingerprint image to be identified and the plurality of seed templates; determining the auxiliary template, of which the overlapping rate with any reference region is greater than an overlapping rate threshold value, in the plurality of auxiliary templates as a supplementary template; and identifying the fingerprint image to be identified through the supplementary template.

A processor-implemented method with image preprocessing includes: transforming a fingerprint image into a frequency domain; suppressing a low-frequency region corresponding to a first noise component of the fingerprint image in the frequency domain; and restoring the frequency domain, in which the low-frequency region is suppressed, as an image.

A fake fingerprint recognition device includes a memory and a processor. The memory is configured to store at least one command. The processor is configured to read the at least one command to execute following steps: receiving an input fingerprint image; calculating a fingerprint index according to the input fingerprint image and a registered fingerprint image in a fingerprint database, wherein the input fingerprint image corresponds to the registered fingerprint image, and the registered fingerprint image is stored in the fingerprint database in advance; determining whether the fingerprint index is larger than a predetermined index threshold; and if the fingerprint index is larger than the predetermined index threshold, determining that the input fingerprint image is a fake fingerprint image.

The embodiments of the present disclosure provide a method and apparatus for generating a fingerprint image to be recognized, and relates to the technical field of ultrasonic fingerprint recognition. A specific embodiment of the method includes the following steps: collecting a fingerprint image of a fingerprint; using a first feature of the fingerprint image to match a target background image for the fingerprint image from a background image library, where the first feature of the fingerprint image is at least one of a signal flight time of an ultrasonic signal for generation of the fingerprint image, a signal transmission frequency of the ultrasonic signal for generation of the fingerprint image, or a collection time of the fingerprint image; and subtracting the target background image from the fingerprint image to obtain a fingerprint image to be recognized of the fingerprint. The embodiment can improve the fingerprint recognition accuracy in various recognition scenarios.

The invention discloses an access control method based on artificial intelligence, and relates to the technical field of access control. When the personnel in the monitoring range stay for a preset time length or press a physical key, triggering an access control request, collecting a video frame containing a human face and extracting a human face image; ambient brightness is measured through a photosensitive sensor, illumination compensation is performed before graying is performed when the ambient brightness is lower than a threshold value, and otherwise, graying is performed directly to obtain a grayed face image; identifying the grayed face image by using a local shielding face identification method, if the identification succeeds, acquiring a personnel ID, and if the identification fails, entering a server side for secondary identification; sending the grayed face image to a server, performing secondary recognition through a deep metric learning algorithm, and if the recognition succeeds, obtaining a personnel ID; and fingerprint composite verification: when any end is successfully identified, acquiring a personnel fingerprint image and acquiring a real-time fingerprint, and opening an access control after successful matching. According to the invention, efficient and safe access control is realized through a dual-biological characteristic cooperation mechanism combining face preliminary verification with fingerprint accurate confirmation.

The invention discloses a hand multi-feature recognition system based on a graph convolutional network. The hand multi-feature recognition system comprises a new user registration module, a user identity verification module, an enhanced recognition module and a logout function module. According to the hand multi-feature recognition system based on the graph convolutional network, an enhanced recognition module is used for collection and preprocessing of finger multi-modal features, and the collection and preprocessing of the finger multi-modal features utilize collection equipment to collect fingerprints and finger veins respectively; the method comprises the steps of collecting a fingerprint image, performing denoising, binaryzation, normalization and region-of-interest enhancement on the collected image, combining a traditional feature extraction mode with an emerging convolutional neural network, and selecting the convolutional neural network to replace a manual design feature extraction algorithm in consideration of the complexity of fingerprint image preprocessing. And a traditional image enhancement algorithm is selected for the vein image with strong directivity. Therefore, the model identification speed is improved, that is, when the mode is converted, a new mode can be quickly adapted only by retraining network parameters.

This application provides an electronic device. The electronic device includes a display panel, a camera, a fingerprint recognition light source, and a light-directing element. The transmittance of a first display area in the display panel is greater than that of a second display area, the second display area including a fingerprint recognition area. The camera, fingerprint recognition light source, and light-directing element are all located on the side of the display panel opposite to its light-emitting surface. The camera includes a photoelectric conversion chip, and the camera is disposed on the side of the first display area opposite to its light-emitting surface. The fingerprint recognition light source is used to direct light towards the fingerprint recognition area during the fingerprint recognition stage. The light-directing element is used to transmit the light reflected by the touch subject pressing the fingerprint recognition area to the photoelectric conversion chip during the fingerprint recognition stage. During the fingerprint recognition stage, fingerprint image information can be acquired using the photoelectric conversion chip of the camera, thereby achieving fingerprint recognition. The photoelectric conversion chip in the camera has high imaging quality and accuracy, which can improve the fingerprint recognition accuracy.

The present invention discloses a fingerprint recognition method based on the fusion of global features and local minutiae features. The present invention comprises the following steps: (1) batch image input and preprocessing; (2) feature extraction using a CvT model network; (3) obtaining minutiae information of the fingerprint image using a CNN convolutional neural network; (4) performing minutiae screening on the obtained minutiae score map by setting a threshold, and finally obtaining minutiae information; (5) performing a test, and performing image similarity evaluation on the trained student network using the obtained global feature map and minutiae information. The present invention aims to achieve high-precision fingerprint recognition by realizing the complementarity of global and local fingerprint information through the fusion of fingerprint global features and minutiae local features.

The invention discloses a rapid magnetic resonance fingerprint reconstruction method and system based on K uniform hypergraph regularization, and relates to the technical field of magnetic resonance fingerprint imaging. The invention aims to solve the problems that the imaging quality is reduced due to data undersampling artifacts in magnetic resonance fingerprint imaging at present, and a non-local irregular anatomical structure is difficult to model and the calculation complexity is high in the existing method. The method comprises the following steps: (1) acquiring undersampled magnetic resonance fingerprint data; (2) constructing a K uniform hypergraph; (3) establishing a magnetic resonance fingerprint reconstruction model based on K uniform hypergraph regularization; (4) constructing an iterative solution algorithm based on an incremental sub-gradient approximation method; (5) setting an iterative solution algorithm termination condition; and (6) reconstructing the magnetic resonance fingerprint data and the quantitative parameter image by using an iterative solution algorithm. Experiments show that under the condition of high-power undersampling, a high-quality magnetic resonance fingerprint image can be reconstructed by using less sampling data, the reconstruction time is shortened, and a more reliable quantitative image basis is provided for clinical disease diagnosis and treatment monitoring.

This application belongs to the field of image acquisition technology and discloses an under-screen fingerprint acquisition optical module and an under-screen fingerprint acquisition method, which comprises a touch screen, a display screen pixel layer, a polygonal gradient refractive index lens array, a micro-hole array plate, and an image sensor in order; the upper surface of the touch screen is arranged on the object-side principal plane of the polygonal gradient refractive index lens array, and the upper surface of the image sensor is arranged on the image-side principal plane of the polygonal gradient refractive index lens array; the fingerprint to be measured is diffusely reflected under the luminous illumination of the display screen pixel layer, and the diffusely reflected light of the fingerprint to be measured passes through the display screen pixel layer and then enters the polygonal gradient refractive index lens array. The light emitted by each sub-lens passes through the micro-hole array plate and is comprehensively imaged on the image sensor. The conjugate images formed by all sub-lenses on the fingerprint to be measured are superimposed and enhanced on the image sensor, effectively improving the imaging light flux, resolution, and image contrast, thereby improving the fingerprint imaging quality.

The application provides a lens system, a fingerprint identification device and a terminal device. The lens system comprises: a first lens and a second lens arranged in sequence from an object side to an image side; the first lens is a negative lens, the first lens has a concave surface facing the object side and a convex surface facing the image side, at least one surface of the first lens is aspherical; the second lens is a positive lens, the second lens has a convex surface facing the image side, at least one surface of the second lens is aspherical; the focal length f1 of the first lens and the focal length f2 of the second lens satisfy -5 < f1 / f2 < -2.61. Through the technical scheme, the surface type and the focal length distribution of the two lenses in the lens system are designed, so that the lens system has better imaging quality. Therefore, the lens system has lower cost, lower structural complexity and smaller size while having better imaging quality, which is conducive to the promotion and use of the lens system in the fingerprint imaging device or other imaging device.

Some methods may involve receiving fingerprint image data. Some methods may involve determining whether the fingerprint image data was captured by a first fingerprint sensor of a device or a second fingerprint sensor of the device. Some methods may involve obtaining an enhanced version of the fingerprint image data based on a determination that the fingerprint image data was captured by the second fingerprint sensor of the device. Some methods may involve providing the enhanced version of the fingerprint image data for authentication. In some examples, obtaining the enhanced version of the fingerprint image data may involve an image enhancement machine learning model. In some examples, prior to obtaining the enhanced version of the fingerprint image data, a determination is made that the fingerprint image data captured by the second fingerprint sensor fails to satisfy a quality threshold.

The present application relates to a kind of multi-modal identity authentication methods based on attention mechanism, comprising: S1, multi-modal data is collected, including face image data, fingerprint image data and voiceprint audio data;S2, for each modality, corresponding first feature extraction model is constructed, second feature extraction model is obtained by cutting first feature extraction model, first feature extraction model is used to carry out knowledge distillation training to second feature extraction model, and third feature extraction model is obtained;S3, the feature extracted by all third feature extraction model is weighted summation using multi-modal feature fusion model based on attention mechanism, and fusion feature is obtained;S4, the similarity between the fusion feature of the identity to be verified and the corresponding identity in the fusion feature of pre-recorded database is calculated, when similarity is greater than prediction decision threshold, then identity verification is passed, otherwise identity verification is not passed.Compared with prior art, the present application has the advantages of efficient feature extraction and high reliability.

The invention discloses a lightweight non-repudiation model fingerprint efficient traceability method, and belongs to the related technical fields of cryptography application, digital content traceability and the like. The method comprises the following specific steps: inputting an original image data set into a computer system; training and learning a lightweight encoder and a decoder, wherein the encoder maps a binary fingerprint and an image to generate a residual error; the residual error is constrained through a Sigmoid function to generate a fingerprint-containing image, and the model is optimized through dual loss; generating metadata of a PIT parameter binding salt value and a commitment value, and embedding the fingerprint into the image; during detection, a decoder is loaded to extract a decoded fingerprint, binaryzation is carried out through a threshold function, and dual verification is realized through fingerprint polynomial comparison and commitment value verification. According to the method, a fingerprint verification mode is converted into polynomial verification through a polynomial equivalent detection technology, the communication complexity is reduced to sub-linear overhead, lightweight efficient verification is realized, and the method is suitable for efficient traceability and authenticity verification of deep forged images.

A method for adjusting the dryness and wetness of an image based on an interpolation algorithm: 1. Define the pixels of the image acquisition unit, acquire a fingerprint image, establish the coordinates for image processing through interpolation operations, and determine the positions of multiple image acquisition units. 2. Perform image processing on multiple image acquisition units, and determine the fingerprint type based on the image processing results. If the fingerprint image is determined to be a neutral fingerprint image, output the fingerprint image; otherwise, proceed to step 3. 3. Based on the wetness and dryness adjustment parameter table, the main controller modifies the corresponding register parameters of the capacitive fingerprint sensor through the SPI interface to adjust the wetness and dryness of the fingerprint images of multiple image acquisition units. The traditional algorithm takes 120ms to acquire an entire image when adjusting the wetness and dryness. By reducing the acquisition area, this algorithm only takes 40ms to acquire an image, greatly speeding up the acquisition speed.

The invention discloses an under-screen fingerprint module automatic alignment calibration system and method based on sensing feedback, and relates to the field of intelligent electronic equipment manufacturing and optical sensing, and the method comprises the steps that a fingerprint recognition module collects a fingerprint image; the position detection sensor units are arranged on the periphery of the fingerprint identification module and are used for detecting offset data of the fingerprint identification module on the X axis, the Y axis and the Z axis based on a screen trepanning theory center coordinate; the control unit outputs corresponding adjusting instructions according to the offset data, detected by the detection position detection sensor unit, of the fingerprint identification module on the X axis, the Y axis and the Z axis; the fine tuning execution mechanism pushes the fingerprint identification module to move to the theoretical center coordinate position of the screen trepanning in the corresponding direction; the calibration reference module stores theoretical center coordinates of screen trepanning; and the locking mechanism controls the position of the fingerprint identification module to be finally locked when the fine tuning execution mechanism pushes the fingerprint identification module to the theoretical center coordinate position of the screen trepanning. According to the invention, the accuracy, the stability and the production yield of fingerprint identification are improved.

The invention discloses a self-adaptive fingerprint identification method and module based on multi-modal imaging, and the method comprises the steps: obtaining a multi-spectral fingerprint image sequence, triggering a plurality of light sources in a time-sharing manner, and collecting a spectrum corresponding to each light source; acquiring a stain type and a regional mask image, inputting the multispectral fingerprint image sequence into a target light convolutional neural network model, and performing stain type identification by the target light convolutional neural network model according to the multispectral fingerprint image sequence; and performing adaptive fingerprint identification, performing image enhancement according to the stain type to obtain a first enhanced image, performing local dynamic exposure compensation on the first enhanced image to obtain a second enhanced image, and transmitting the second enhanced image to a fingerprint identification unit for identification. The fingerprint recognition accuracy is improved, and the problems that the effect is poor when a traditional enhanced image software algorithm is used for recognition, and the fingerprint recognition accuracy is low due to the fact that dynamic stain type judgment is not combined are solved.