624 results about "Fingerprint image" patented technology

The present disclosure provides a system for IoT Botnet detection, comprising: a flow preprocessing device for processing the flow data packets to obtain NetFlow data; a flow clustering device for segmenting the NetFlow data to obtain flow points, and clustering the flow points to form botnet transactions or normal behavior transactions; a fingerprint generation device for extracting feature fingerprints from the obtained transactions and converting the feature fingerprints into RGB fingerprint images; and a classification device for detecting the fingerprint images and yielding the classification results.

The invention discloses a fingerprint image restoration method based on a conditional diffusion probability model, and relates to the technical field of security and identity authentication, and the method comprises the following steps: firstly constructing a fingerprint data set containing various fingerprint features, and carrying out the amplification of the fingerprint data set; secondly, performing threshold segmentation on the original fingerprint image to obtain a foreground region containing fingerprint information, namely a to-be-repaired fingerprint image; constructing and training an unconditional diffusion probability model to generate fingerprint prior information; and finally, inputting the known area pixel value and the mask information of the fingerprint image to be repaired into the reverse iteration denoising network as condition information to guide the generation of the fingerprint image. According to the method, rich semantic information of the fingerprint image can be better learned, and the method has relatively strong generalization ability for masks in any shape, so that the repaired fingerprint image is reasonable in structure and clear in texture; subjective and objective test evaluation results and quantitative contrast experiments prove the effectiveness of the method.

The invention discloses a sound wave positioning device and method for a coal mine auxiliary transportation digital twin system. According to the device, two loudspeakers are installed on the side wall of a roadway of an auxiliary transportation equipment operation area at intervals; the omnidirectional microphone and the edge computing node are mounted on the auxiliary transportation equipment; the method comprises installing the sound wave positioning device; constructing an asymmetric sound field; off-line RSS acquisition is carried out; constructing a multi-dimensional RSS fingerprint vector according to the received interference intensity, and constructing a gradient fingerprint image based on the multi-dimensional RSS fingerprint vector; establishing a fingerprint database; acquiring an RSS vector Rtest of a current to-be-positioned point, converting the RSS vector Rtest into a corresponding to-be-matched gradient fingerprint image gtest, and performing fingerprint matching by using a fingerprint image matching algorithm based on a discrete Frechet distance to obtain position information; and digital twinning integration. According to the device and the method, the auxiliary transportation equipment can be quickly and accurately positioned in a complex coal mine environment, and the safety and the efficiency of mine operation can be effectively improved through real-time feedback and visual monitoring.

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 discloses a multi-level network identity verification and access control system, which is characterized in that a biological feature recognition technology is further introduced after traditional password verification, and multi-scale feature extraction is carried out on a fingerprint image input by a user by utilizing an image processing technology based on deep learning; shallow detail features and global structure features of fingerprints are captured, and comprehensive feature representation of a fingerprint image is obtained through feature selection enhancement and multi-level feature joint perception processing, so that a defogged fingerprint image is generated, and based on pixel-by-pixel fingerprint difference information between the defogged fingerprint image and an authorized fingerprint image, the defogged fingerprint image is obtained. A final verification result is generated, and it is ensured that only the verified user can access the system or data conforming to the role of the user. Through the mode, the problem of poor fingerprint image quality can be effectively solved, so that the accuracy and safety of identity verification are improved, and the overall performance of the system and the user experience are optimized.

The invention discloses a multi-element antibiotic rapid detection method based on high-throughput array spectrum and deep learning, and belongs to the technical field of water environment new pollutant detection. The method comprises the following steps of antibiotic spectrum sensing system building, combined chemical probe screening, high-throughput spectrum experiment, deep learning stoichiometric model development and antibiotic intelligent detection system development. According to the invention, a high-throughput combined array spectrum sensing technology is adopted, holographic spectrum imaging is taken as a core to obtain fingerprint image data, the experiment efficiency is greatly improved, a high-throughput experiment and a machine learning model are combined, the sampling range is expanded, more types of chemical mixture combinations can be covered, and the detection accuracy is improved. The deep learning model is good at extracting complex features from a large amount of data, fingerprint spectrums of different types of antibiotics can be accurately identified and distinguished, high sensitivity and high specificity can be kept even in a complex water environment, and the accuracy and response speed of pollutant detection in a complex chemical system are improved.

The invention relates to the technical field of access control systems, and discloses a multi-mode biological recognition access control system, a biological recognition module is used for collecting biological characteristic images of a user, and the biological characteristic images at least comprise a fingerprint image, a face image and an iris image; the data processing module is used for performing data processing and analysis on the biological feature image acquired by the biological recognition module, extracting an effective feature value, and obtaining a target feature image; the identity determination module is used for performing multi-mode identification on the target feature image to obtain an identification result, and performing comparison to determine the identity of the user; the control module is used for controlling the opening or closing of the access control according to the comparison result of the identity determination module; the storage module is used for storing a biological characteristic template and access control parameter information of a user; the communication module is used for carrying out communication and linkage with a mobile terminal in a wired communication or wireless communication mode; the safety of the access control system is effectively improved, and the accuracy of identity verification is 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.

An electronic device, method, and computer program product automatically support fingerprint authentication using a fingerprint scanner on an opposite side even when the electronic device is detected as being grasped upside down. The electronic device includes front and back fingerprint scanners respectively exposed on front and back sides of a device housing. In response to determining that a requirement exists for scanning a fingerprint of a user while the electronic device is upside down, a controller of the electronic device triggers at least one of the front and the back fingerprint scanners to initiate scanning. The controller receives a fingerprint image from a corresponding one of the front and back fingerprint scanner. The controller authenticates the user in response to identifying that an upside-down version of the received fingerprint image corresponds to a fingerprint of an authorized user, based on authentication data stored in memory of the electronic device.

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.

The invention discloses a fingerprint identification method and system based on an optical identification induction technology, and relates to the technical field of fingerprint identification, and the method comprises the steps: collecting a fingerprint surface texture image through the total reflection principle of light; converting the collected fingerprint surface texture image into a fingerprint surface texture grayscale image; determining the main direction and frequency of the fingerprint streaklines, and separating the adhered fingerprint streaklines; smoothing stray point information in the fingerprint image by using an image filtering algorithm; the fluctuation characteristics of the fingerprints are further highlighted by using morphological expansion and corrosion operation; extracting morphological features of fingerprints and local features of streaklines from the preprocessed fingerprint image; and outputting the user information corresponding to the fingerprints in the database higher than the preset similarity value. By arranging the image acquisition module, the image processing module, the feature extraction module and the similarity comparison module, the interference of environmental factors is reduced, the fingerprint image quality is ensured, the fingerprint identification time is shortened, and the real-time performance of the system 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 invention provides a method and a system for carrying out joint verification on a digital identity by using a combined model, and relates to the field of intelligent verification. And an image processing and analysis algorithm based on artificial intelligence and deep learning is introduced at the rear end to analyze the face image and the fingerprint image, so as to capture and describe a multi-modal identity joint representation between the face feature and the fingerprint feature of the to-be-verified object, and the identity joint representation is used to verify the digital identity. And judging whether the to-be-verified object belongs to the authorized object or not. Therefore, joint verification can be performed on the digital identity more intelligently through the combination of the face recognition base model and the fingerprint recognition base model, so that the intelligent level of identity verification is improved, the safety of the system is enhanced, and the risk of false use of the identity is prevented.

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.

A fingerprint recognition method, an electronic device and a storage medium. In the method, when a fingerprint image collected by an electronic device is a wet finger image of a user's finger that is wet, and the wet finger image fails to match a fingerprint template in a fingerprint template library, the electronic device can optimize the wet finger image to obtain an optimized fingerprint image. The electronic device then matches the optimized fingerprint image with the fingerprint template. By implementing the provided technical solution, when fingerprint recognition is performed on a user's finger that is wet, a fingerprint of the user can also be successfully recognized. Thus, when a user's finger is wet, an electronic device can also be successfully unlocked.

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.

The invention discloses a liquid crystal display screen fingerprint module reading method, system and device and a storage medium. The method comprises the following steps: acquiring an initial fingerprint image of a liquid crystal display screen fingerprint module; performing preprocessing operation on the initial fingerprint image to obtain a second fingerprint image; inputting the second fingerprint image into a pre-trained liquid crystal display screen fingerprint reading processing model to obtain a third fingerprint image; inputting the initial fingerprint image and the third fingerprint image into a pre-trained fingerprint image correction model based on high dynamic range image fusion when the effective feature points contained in the third fingerprint image do not meet a preset feature point threshold value, and obtaining a final result image. And performing fingerprint reading according to the final result image. The method has the following effects: when fingerprint damage or environment illumination change causes an image quality problem, the detail features of the fingerprint can be accurately extracted and matched.

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 fingerprint image definition detection method and device. The method comprises the steps of collecting an original fingerprint image; dividing the original fingerprint image into a plurality of first image blocks, determining each first abnormal block from each first image block, and counting the number of the first abnormal blocks; if the number of the first abnormal blocks is smaller than a first preset number, performing color inversion processing on the original fingerprint image to obtain a color-inverted image; dividing the inverse color image into a plurality of second image blocks, determining each second abnormal block from each second image block, and determining the number of the second abnormal blocks; if the number of the second abnormal blocks is smaller than the second preset number, selecting each center block from each second image block according to the preset number; and determining the definition type of each center block, and determining the definition type of the original fingerprint image based on the definition of each image block. The detection accuracy can be improved, and time and resources are saved.