114results about "Detecting live finger character" patented technology

Embodiments of the present application provide a fingerprint anti-counterfeiting neural network training method, a fingerprint anti-counterfeiting method and device, comprising: obtaining multiple sets of training data, each set of training data including: first native domain data, second native domain data and third native domain data; using the multiple sets of training data to train an initial classification network to obtain a target classification network, the initial classification network including a fusion subnetwork and a classification subnetwork, for each set of training data, the fusion subnetwork is used to generate a first fingerprint matching pair based on the feature description matrix of the first native domain data and the second native domain data, and generate a second fingerprint matching pair based on the feature description matrix of the first native domain data and the third native domain data, and the classification subnetwork is used for fingerprint classification and identification based on the first fingerprint matching pair and the second fingerprint matching pair. Through the present application, the problem of low identification accuracy of true and false fingerprints in related technologies is solved, and the effect of improving the identification accuracy of true and false fingerprints is achieved.

The invention relates to the technical field of living body fingerprint detection, and particularly discloses a living body fingerprint detection method based on multispectral and micro pulse feature fusion, which comprises the steps of signal synchronous generation, time domain alignment processing, feature matrix generation, weight dynamic adjustment, cross validation analysis, abnormal region sampling, living body model verification and living body authentication output. According to the method, the verification result is generated by synchronously collecting the multi-spectral reflection intensity and the skin displacement fluctuation sequence of the fingerprint area, dynamically adjusting the spectral absorption and pulsation track feature weight, performing cross validation to output the local abnormal area and obtain the supplementary feature data, and performing physiological coupling rule matching by using the preset living body judgment model. When three consecutive results do not conform to a living body rule, activating an anti-counterfeiting attack alarm protocol; according to the method, multispectral and micro pulse features are fused, and an environment adaptive weight mechanism is combined, so that the accuracy of living body detection is effectively improved, and the environment adaptability of a detection algorithm is enhanced.

The invention relates to the technical field of payment security, and particularly discloses a security payment system and method based on biological recognition. The method is characterized by comprising the following steps: synchronously acquiring fingerprint, finger vein and pressure behavior characteristics through a coaxial integrated sensor; a dynamic encryption engine is adopted to bind the biological characteristics with the transaction parameters to generate a one-time payment token; living body verification is realized based on physiological synchronism of vein pulsation and pressure fluctuation; and establishing a user pressing behavior baseline model to identify abnormal operation. The terminal equipment is provided with a sapphire microlens array and a dynamic pressure-sensitive array. The problems of biological feature forgery, replay attack and living body cheating are solved, the forgery detection rate reaches 99.6%, and the false identification rate is smaller than or equal to 0.0001%.

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.

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 a data processing method and device and a program product. Relates to the field of artificial intelligence or financial science and technology, and the method comprises the steps: collecting face feature data in a face image or a face video when a financial service is handled, inputting the face feature data into a target recognition model, and obtaining a recognition result, the recognition result is used for indicating whether a target object in the face image or the face video is a moving object; under the condition that the recognition result indicates that the target object in the face image or the face video is a moving object, collecting static biological characteristics of the target object to obtain static characteristic data, and collecting dynamic behavior characteristics of the target object to obtain dynamic characteristic data; and performing anomaly detection on the static feature data and the dynamic feature data to obtain a target detection result. According to the method and the device, the problem of low security of security verification in a financial business handling process in the prior art is solved.

Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using neural-network-based anti-spoofing protection mechanisms. An example method generally includes receiving an image of a biometric data source for a user; extracting, through a first artificial neural network, features for at least the received image; combining the extracted features for the at least the received image and a combined feature representation of a plurality of enrollment biometric data source images; determining, using the combined extracted features for the at least the received image and the combined feature representation as input into a second artificial neural network, whether the received image of the biometric data source for the user is from a real biometric data source or a copy of the real biometric data source; and taking one or more actions to allow or deny the user access to a protected resource based on the determination.

The present application provides a fingerprint anti-counterfeiting processing method and apparatus, a medium, a system and a device, and relates to the field of fingerprinting. The method includes: obtaining a fingerprinting request which includes a fingerprint to be identified; obtaining a preset first anti-counterfeiting index value corresponding to the fingerprint to be identified or a binary classification model established on the basis of the first anti-counterfeiting index value, and determining whether the fingerprint to be identified is a forged fingerprint on the basis of the first anti-counterfeiting index value or the binary classification model; wherein the first anti-counterfeiting index value is a value preset on the basis of obtained feature parameter values corresponding to the fingerprint to be identified and taken as a target fingerprint in different time sequences. Through the above method, according to the present application, the forged fingerprint may be efficiently identified, so that safety of fingerprinting is improved.

A contactless biometric sensor, including an upper face above which, in use, a human body part which has dermatoglyphs is positioned, a lighting source configured to light the human body part through the upper face and an imager configured to acquire an image of the human body part through the upper face. The upper face of the sensor is provided with a mask having spatially variable light transmissivity.

This disclosure relates to a fingerprint recognition processing method, apparatus, electronic device, and storage medium thereof. The fingerprint recognition processing method includes: when it is determined that the anti-mistouch recognition result of the fingerprint unlocking area in the electronic device is a mis-touch of a non-fingerprint object, determining the number of mis-touches of the non-fingerprint object based on the hardware abstraction layer of the electronic device; if it is determined based on the number of mis-touches that the electronic device does not meet the anti-mistouch detection retry conditions, then the application framework layer of the electronic device receives the mis-touch result information reported by the hardware abstraction layer; in response to the mis-touch result information meeting preset conditions, triggering a fingerprint lockout mechanism to not respond to fingerprint unlocking commands for a preset time period. This disclosure can avoid frequent lockouts of electronic devices.

An electromagnetic wave sensor device according to an aspect of the disclosure, comprising: a first sensor configured to perform an output in accordance with electromagnetic waves absorbed by a first quantum dot, an upper limit wavelength of electromagnetic waves absorbable by the first quantum dot being a first wavelength, a second sensor configured to perform an output in accordance with electromagnetic waves absorbed by a second quantum dot different from the first quantum dot, an upper limit wavelength of electromagnetic waves absorbable by the second quantum dot being a second wavelength longer than the first wavelength; and a calculation unit configured to calculate a difference between the output of the second sensor and the output of the first sensor.

A method for driving a display device is performed by the display device. The method includes: displaying a first image including user convenience information through a display panel; generating sensing data corresponding to a fingerprint sensing area by using a photoelectric sensor; and determining whether a sensed fingerprint corresponding to the sensing data is a fake fingerprint by comparing expected sensing light illuminance information based on the first image with sensed light illuminance information of the sensing data. The first image includes a first color pattern in the fingerprint sensing area of the display panel. A fake-determination image pattern includes the first color pattern and a second color pattern different from the first color pattern.

The invention provides a presentation attack detection method comprising: - obtaining (300) a set of N biometrics, N being an integer equal to or greater than 2; - for each biometric of the set, extracting (301) at least one feature of the biometric; - determining (302) at least one relationship value for the set of N biometrics by applying a comparison function to the extracted features; - determining (303) a relationship score based on the at least one relationship value, the relationship score being representative of a genetical relationship between the biometrics of the set; - determining (304) a first classification of the set of N biometrics based on the obtained relationship score, the first classification indicating that the set of N biometrics is genuine or that the set of N biometrics is a presentation attack.

The embodiment of the invention provides a palm living body recognition method and device and electronic equipment, and the method comprises the steps: carrying out the palm key point detection of an input image, and recognizing palm key points in the input image; cutting the input image based on the palm key points to obtain at least one local image corresponding to the input image; each local image comprises at least one palm key point; and obtaining a target palm living body recognition result corresponding to the input image based on the at least one local image by using a target palm living body recognition model. Palm living body recognition based on the features of the local region of the palm is realized, so that the target palm living body recognition result corresponding to the input image is obtained, and the problem that the feature difference of different regions of the palm cannot be distinguished when living body recognition is performed by using the features of the global image of the palm image is avoided; the feature information of different areas of the palm can be fully utilized, so that the accuracy of palm living body recognition is improved.

A photo-capacitance sensor (29) includes an input surface (30) and one or more light sources (31) arranged to illuminate a portion (35) of the input surface (30). The photo- capacitance sensor (29) also includes an array (36) of photo-capacitors arranged (32) to receive light (38) from the one or more light sources (31) which is reflected from an object (39) in contact with, or proximate to, the illuminated portion (35) of the input surface (30). The array (36) of photo-capacitors (32) is configured for detecting a reflective pattern of the object (39).

An image processing method, including: obtaining an original image, the original image including biological features of a to-be-detected object, fusing a global frequency domain map corresponding to the original image with the original image to obtain a global fusion image, performing living body detection on the global fusion image to obtain a first detection result, determining that the original image does not pass the living body detection when the first detection result indicates that the original image belongs to a screen reproduced image, obtaining, when the original image does not belong to the screen reproduced image, a biological feature image based on the biological features, fusing a local frequency domain map with the biological feature image to obtain a local fusion image, performing living body detection on the local fusion image to obtain a second detection result, and determining a living body detection result corresponding to the original image.

A sensor, a device, a system, and a method, for biometric sensing, are provided. Such a device or system includes a micro heater and a micro temperature sensor for at least active thermal sensing, which may comprise an oxide semiconductor material. The micro heater and the micro temperature sensor may be separate or combined in one pixel. The present disclosure also provides an out-cell type or an in-cell type of biometric sensor on display device, for example, an out-cell type or an in-cell type fingerprint sensor on display (FoD) device. The pixels for active thermal sensing include an oxide semiconductor material. The methods of making and the methods of using the sensors, the devices, or the system are also provided.

An apparatus may include an ultrasonic sensor system having an ultrasonic transceiver layer, a thin-film transistor (TFT) layer and a frequency-differentiating layer. In some examples, the frequency-differentiating layer may include a first frequency-differentiating layer area corresponding to a lower-frequency area of the ultrasonic sensor system. The first frequency-differentiating layer area may include a first material having a first acoustic impedance. In some such examples, the frequency-differentiating layer may include a second frequency-differentiating layer area corresponding to a higher-frequency area of the ultrasonic sensor system. The second frequency-differentiating layer area may include a second material having a second acoustic impedance. The first acoustic impedance may, for example, be higher than the second acoustic impedance.

A sensor, a device, a system, and a method, for biometric sensing, are provided. Such a device or system includes a micro heater and a micro temperature sensor for at least active thermal sensing, which may comprise an oxide semiconductor material. The micro heater and the micro temperature sensor may be separate or combined in one pixel. The present disclosure also provides an out-cell type or an in-cell type of biometric sensor on display device, for example, an out-cell type or an in-cell type fingerprint sensor on display (FoD) device. The pixels for active thermal sensing include an oxide semiconductor material. The methods of making and the methods of using the sensors, the devices, or the system are also provided.

A device may include an ultrasonic sensor system having a first layer stack and a second layer stack. The first layer stack may include a first ultrasonic transmitter, and the second layer stack may include a second ultrasonic transmitter. The first layer stack and / or the second layer stack may include an ultrasonic receiver. A frequency division layer may be located between the first layer stack and the second layer stack.

The embodiments of the present application provide a fingerprint recognition method, a fingerprint recognition device, an electronic device, and a storage medium. The fingerprint recognition method includes: when the fingerprint recognition area is touched by the fingerprint to be recognized, collecting a continuous first frame fingerprint image and a second frame fingerprint image; comparing the first frame fingerprint image and the second frame fingerprint image to determine the changes in the first frame fingerprint image and the second frame fingerprint image, and determining whether the fingerprint to be recognized is a real fingerprint based on the changes in the first frame fingerprint image and the second frame fingerprint image. This embodiment continuously collects two frames of fingerprint images during each fingerprint recognition, and determines whether the fingerprint to be recognized is a fake fingerprint based on the changes in the two fingerprint images, thereby eliminating the interference of fake fingerprints and improving the security of fingerprint recognition.

The invention relates to a contactless biometric sensor (1), comprising an upper surface (3) above which, in use, a part of the human body bearing dermatoglyphs is positioned, a light source configured to illuminate said part of the human body through the upper surface, and an imager configured to acquire an image (I) of said part of the human body through the upper surface. The upper surface of the sensor is provided with a mask of spatially variable light transmissivity. Figure for the abstract: Figure 1

There is provided a method for training a fake fingerprint detection model, performed by a training device for the fake fingerprint detection model, the method comprising: acquiring a physical fake fingerprint image generated in a physical manner targeting a finger; providing generation constraints and a unique identification information to a training fingerprint image generation model; generating a training fingerprint image by using the training fingerprint image generation model, so that the unique identification information of the training fingerprint image is mapped onto a real fingerprint of the finger; and training the fake fingerprint detection model using the physical fake fingerprint image and the training fingerprint image in a transfer learning manner or an ensemble learning manner.

An acoustic biometric touch scanner device and method is disclosed. In one aspect, an acoustic fingerprint sensing device includes an array of ultrasonic transducers configured to transmit an ultrasound signal having a frequency in a range from 50 megahertz (MHz) to 500 MHz. The acoustic fingerprint ultrasonic transducers include a piezoelectric film. The acoustic fingerprint sensing device further includes a receiving surface configured to receive a finger. The acoustic fingerprint sensing device further includes a processor configured to generate an image of at least a portion of a fingerprint of the finger based on a reflection of the ultrasound signal from the finger.

A fingerprint identification method and a fingerprint sensing circuit thereof are disclosed. The fingerprint identification method is used in a fingerprint sensing circuit and includes the following steps: performing a first fingerprint sensing procedure under a first light spot setting, the first fingerprint sensing procedure including a first exposure operation and a first readout operation; and performing a second fingerprint sensing procedure under a second light spot setting, the second fingerprint sensing procedure including a second exposure operation and a second readout operation. A host performs a first fingerprint identification corresponding to the first fingerprint sensing procedure, and the second light spot setting is performed simultaneously with the first fingerprint identification.

An anti-spoofing method includes detecting first information related to whether the biometric information is forged, based on a first output vector of a first neural network configured to detect whether the biometric information is forged from the input data, extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information, calculating a similarity value of the input embedding vector based on a fake embedding vector and either one or both of a real embedding vector and an enrollment embedding vector that are provided in advance, calculating a total forgery score based on the similarity value and a second output vector of the first neural network according to whether the first information is detected, and detecting second information related to whether the biometric information is forged, based on the total forgery score.

The present invention relates to a device and methods for the acquisition and processing of touchless fingerprints of one or more fingers, which can be used for biometric (1:N) identification and / or (1:1) verification of users in applications related to the field of security systems and access control in offices, large corporate buildings, industry, hospitals, universities, luxury residential environments, etc. The device houses all the system components inside a compact compartment of reduced dimensions and bestows both a highly-innovative mechanics and an ergonomic design, which is underpinned by a open-capture area that lacks covers, visors, or supporting parts to place the hand or fingers in the tridimensional space. The fingers are captured "in the air" (without contact with the device) in a rapid, hygienic, simple and intuitive way, while the user is guided where to properly locate the hand (fingers) during the capture process through an acoustic-luminous signaling system. The device comprises at least an external mechanical enclosure and electronic modules for the illumination, image capturing, object detection, acoustic-luminous signaling, image and data processing, and control. In addition to biometric fingerprint recognition (of one or more fingers), the device allows the capture of other digital access credentials (multi-credential approach), such as RFID proximity cards and smartphones (mobile credential).

The invention provides a fingerprint identification method and device. The fingerprint identification method comprises the following steps: identifying detail feature points in a fingerprint image; determining patches respectively corresponding to the identified detail feature points to obtain a first patch set; a plurality of patches are selected from the first patch set in the direction from the center of the fingerprint image to the edge of the fingerprint image to serve as a second patch set, and the area of an overlapping area between any two patches in the second patch set is smaller than a first threshold value; and identifying the fingerprint image by using the trained model based on the second patch set.

The invention relates to a method for detecting fingerprints using a 2D image-capturing unit (1) and a 3D image-capturing unit (3), wherein a three-dimensional capture of a finger (Fi) is created using the 3D image-capturing unit (3), wherein the image of the finger (Fi) is determined in the three-dimensional capture, wherein the object distance (di) between the 3D image-capturing unit (3) and the finger (Fi) is determined on the basis of the 3D coordinates of the determined image, wherein the 2D image-capturing unit (1) is adjusted to a focal plane corresponding to the determined object distance (di) and the position and / or location in the 3-dimensional capture, wherein a two-dimensional capture (Bj) of the finger (Fi) is created in the previously adjusted focal plane, wherein the position and / or location of the determined image of the finger (Fi) is mapped onto the two-dimensional capture (Bj), and wherein a finger image (FBi,j) and / or fingerprint (FPi,j) is determined from the region corresponding to the mapped position and / or location.