162results 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.

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 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.

The invention discloses a UnionPay electronic payment method. The method comprises the following steps: a user initiates a payment request; dynamically verifying the user identity; transaction encryption processing; payment gateway processing; performing bank authorization; transaction confirmation and fund settlement; payment completion notification; intelligent risk control interception; a payment data query system comprises a transaction query module, a payment state query module, a refund query module, a report module, a storage module, a classification index engine and a privacy protection module. According to the method, a leading electronic payment infrastructure is constructed through fusion connection of biometric authentication, quantum encryption, intelligent risk control, privacy calculation and the like, the security of transaction data is relatively high, multi-dimensional indexing is performed through a classification indexing engine, query of index transaction data can be performed according to multiple indexes, and the security of the transaction data is improved. And the privacy protection module is used for performing local encryption processing on the user data and uploading an aggregation analysis result to a cloud end so as to avoid leakage of the original data.

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.

A liveness detection method and apparatus, a computer device, and a storage medium, relating to the technical field of computers. The method comprises: acquiring a first palm bones and joint image, the first palm bones and joint image comprising palm bones and joint soft tissue between the palm bones (201); performing super-resolution processing on the first palm bones and joint image to obtain a second palm bones and joint image, the resolution of the second palm bones and joint image being greater than the resolution of the first palm bones and joint image (202); acquiring palm features corresponding to the second palm bones and joint image (203); and, on the basis of the palm features, determining a discrimination result, the discrimination result being used for representing whether the first palm bones and joint image is a live palm bones and joint image, and the live palm bones and joint image referring to a palm bones and joint image obtained by performing image capture on a real palm (204).

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.

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.

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 method of online training of a classifier includes determining a distance from one or more feature vectors of an object to a first predetermined decision boundary established during off-line training for the classifier. The method also includes updating a decision rule as a function of the distance. The method further includes classifying a future example based on the updated decision rule.

The present invention improves an accuracy to discern a fake finger created by attaching a transparent thin film to the surface of a finger. The present invention has: a mounting surface that has a mounting area to mount an authentication target that is an object of fingerprint authentication; a transparent plate that is provided on mounting surface and defines a range of an image used for determining the authenticity of the authentication target; a light source that allows light to directly enter the authentication target mounted on the mounting area, not by way of the transparent plate; and an imaging device that images the authentication target mounted on the mounting area, by way of a the transparent plate, wherein a light emitting surface for emitting light of the light source to outside is disposed on the same plane as the mounting surface.

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.

Disclosed are a fingerprint recognition device and a driving method thereof, wherein the fingerprint recognition device includes a sensing panel, a scan drive unit and a timing controller, wherein the sensing panel includes a plurality of sensing blocks, wherein each of the sensing blocks includes a plurality of sensors, the scan drive unit is configured to provide a scan signal to the sensors, the timing controller is configured to provide a second start signal to the scan drive unit, wherein each of the sensors includes a reference capacitor and a sensing capacitor, the scan drive unit includes a scan driver corresponding to a row of the sensing blocks, in a fingerprint recognition mode, the timing controller provides the second start signal to the scan driver corresponding to the sensing block generating a touch input, and in a touch mode, the timing controller sequentially provides the second start signal to the scan driver.

Technologies are presented herein in support of a system and method for performing fingerprint recognition. Embodiments of the present invention concern a system and method for capturing a user's biometric features and generating an identifier characterizing the user's biometric features using a mobile device such as a smartphone. The biometric identifier is generated using imagery captured of a plurality of fingers of a user for the purposes of authenticating / identifying the user according to the captured biometrics and determining the user's liveness. The present disclosure also describes additional techniques for preventing erroneous authentication caused by spoofing. In some examples, the anti-spoofing techniques may include capturing one or more images of a user's fingers and analyzing the captured images for indications of liveness.