Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Abstract: A liveness detection method includes acquiring a facial image of an object and a reference feature set that includes a plurality of reference features conforming to a pre-set feature distribution; extracting an image feature from the facial image, performing liveness feature mapping on the plurality of reference features to obtain a plurality of corresponding reference liveness features, performing, based on the plurality of reference liveness features, liveness discrimination on the image feature to obtain a discrimination result, performing, based on the image feature, distribution fitting on the plurality of reference features to obtain a distribution fitting result, and performing liveness detection on the object based on the discrimination result and the distribution fitting result.

Abstract: An identity recognition method includes: acquiring biometric pattern information describing a biometric pattern of a first object; performing feature extraction on the biometric pattern information to obtain a global pattern feature and a local pattern feature; fusing the global pattern feature and the local pattern feature to obtain a fused pattern feature of the first object; and performing identity recognition on the first object based on the fused pattern feature of the first object.

Abstract: A face liveness detection method is performed by an electronic device. The method includes: obtaining an initial face image of an object and a face image set of the object captured at N different illumination directions; obtaining N difference images according to the initial face image and the N face images in the face image set; generating a normal map and an albedo map according to the N difference images and the N illumination directions; and determining a face liveness detection result according to the N difference images, the normal map, and the albedo map, the face liveness detection result indicating whether the object has a live face or not. In this application, three-dimensional (3D) geometric information and surface material information of a face image are considered, thereby recognizing the authenticity of the face image, and effectively resisting different face liveness attack manners.

Abstract: A method for generating a palmprint sample includes: generating positioning point data according to a palmprint principal line distribution rule, the positioning point data comprising first data corresponding to a first principal line positioning point and second data corresponding to a second principal line positioning point; generating adjusting point data according to a palmprint principal line curving rule, a principal line adjusting point corresponding to the adjusting point data being configured to control a curving parameter of a principal line formed by the first principal line positioning point and the second principal line positioning point; generating a palmprint principal line based on the first data, the second data and the adjusting point data; and generating at least one palmprint sample comprising the palmprint principal line.

Abstract: A liveness detection method includes: obtaining a reflected audio signal and video data of a object in response to receiving a liveness detection request; performing signal processing and time-frequency analysis on the reflected audio signal to obtain time-frequency information of a processed audio signal, and extracting motion trajectory information of the object from the video data; respectively extract features from the time-frequency information and the motion trajectory information to obtain an audio feature and a motion feature of the object; calculating first global attention information of the object according to the audio feature, and calculating second global attention information of the object according to the motion feature; and fusing the first global attention information with the second global attention information to obtain fused global information, and determining a liveness detection result of the object based on the fused global information.

Abstract: An image authenticity detection method includes: obtaining a target image; inputting the target image into a generator of a generative adversarial network, and outputting an artifact image corresponding to the target image through the generator, where the artifact image is used for representing a difference between the target image and a real image; the generator is configured to output a prediction artifact image corresponding to a sample image and generate a fitting image based on the prediction artifact image; and the generative adversarial network further comprises a discriminator in a training stage, and the discriminator is configured to discriminate the authenticity of the fitting image, to assist the generator to learn a difference feature between a false image and a real image; and determining an authenticity detection result of the target image based on the artifact image.

Abstract: An image attack detection method includes: acquiring an image-to-be-detected, and performing global classification recognition based on the image-to-be-detected to obtain a global classification recognition result; performing local image extraction randomly based on the image-to-be-detected to obtain a target number of local images, the target number being obtained by calculation according to a defensive rate of a reference image corresponding to the image-to-be-detected; performing local classification recognition based on the target number of local images respectively to obtain respective local classification recognition results, and fusing the respective local classification recognition results to obtain a target classification recognition result; and detecting a similarity between the target classification recognition result and the global recognition result, and determining the image-to-be-detected as an attack image when the target classification recognition result and the global classification recognit

Abstract: A method and apparatus for extracting biological features, a device, a medium, and a program product, relating to the field of image processing. The method includes generating an image sequence according to a biological feature image, the image sequence including the biological feature image and n local image blocks, the local image blocks being obtained by segmenting the biological feature image, n being a positive integer, generating a feature vector group based on the image sequence, feature vectors in the feature vector group being generated based on a feature information interaction result between the biological feature image and the local image blocks and a feature information interaction result between the local image blocks, and taking a feature vector corresponding to the biological feature image in the feature vector group as biological feature information of the biological feature image.

Abstract: 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.

Abstract: An image detection method includes obtaining a facial image, and obtaining a frequency-domain image of the facial image and a spatial-domain feature of the facial image, the frequency-domain image being obtained by performing frequency-domain transformation on the facial image. The spatial-domain feature is obtained by performing feature extraction on the facial image. The method further includes performing feature extraction based on the frequency-domain image, to obtain a frequency-domain feature of the facial image, and fusing the spatial-domain feature and the frequency-domain feature by using an attention fusion network of a facial image detection model, to obtain a fused feature of the facial image. The method further includes obtaining a detection result of the facial image based on the fused feature, the detection result indicating whether the facial image is a forged facial image.

Abstract: A liveness detection method is provided. In the method, image features are extracted from an image of a user. Convolution processing is performed on the image features through an estimation network to obtain a predicted mean value and a predicted variance of the image features. Standardization processing is performed on the image features based on the predicted mean value, the predicted variance, and predetermined network parameters of the standardization processing to obtain standardized features. Whether the image of the user includes a living body image is determined according to a liveness classification probability of a classification performed on the image of the user based on the standardized features. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium.

Abstract: A method of living body detection includes generating a detection interface in response to a receipt of a living body detection request for a verification of a detection object. The detection interface includes a first region with a viewing target for the detection object to track, a position of the first region in the detection interface changes during a detection time according to a first sequence of a position change of the first region. The method also includes receiving a video stream that is captured during the detection time, determining a second sequence of a sight line change of the detection object based on the video stream, and determining that the detection object is a living body at least partially based on the second sequence of the sight line change of the detection object matching the first sequence of the position change of the first region.

Abstract: A face liveness detection method is performed by an electronic device. The method includes: obtaining an initial face image of an object and a face image set of the object captured at N different illumination directions; obtaining N difference images according to the initial face image and the N face images in the face image set; generating a normal map and an albedo map according to the N difference images and the N illumination directions; and determining a face liveness detection result according to the N difference images, the normal map, and the albedo map, the face liveness detection result indicating whether the object has a live face or not. In this application, three-dimensional (3D) geometric information and surface material information of a face image are considered, thereby recognizing the authenticity of the face image, and effectively resisting different face liveness attack manners.

Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Abstract: A face liveness recognition method includes: obtaining a target image containing a facial image; extracting facial feature data of the facial image in the target image; performing face liveness recognition according to the facial feature data to obtain a first confidence level using a first recognition model, the first confidence level denoting a first probability of recognizing a live face; extracting background feature data from an extended facial image, the extended facial image being obtained by extending a region that covers the facial image; performing face liveness recognition according to the background feature data to obtain a second confidence level using a second recognition model, the second confidence level denoting a second probability of recognizing a live face; and according to the first confidence level and the second confidence level, obtaining a recognition result indicating that the target image is a live facial image.

Abstract: A device receives an image-based authentication request from a specified object and performs human face authentication in a manner depending on whether the object wears glasses. Specifically, the device designates a glasses region on a daily photograph of the specified object using a glasses segmentation model. If the regions of the human face in the daily photograph labeled as glasses exceed a first threshold amount, the device modifies the daily photograph by changing pixel values of the regions that are labeled as being obscured by glasses. The device extracts features of a daily human face from the daily photograph and features of an identification human face from the identification photograph. The device approves the authentication request if a matching degree between the features of the daily human face and the features of the identification human face is greater than a second threshold amount.

Abstract: This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium.

Abstract: A facial tracking method is provided. The method includes: obtaining, from a video stream, an image that currently needs to be processed as a current image frame; and obtaining coordinates of facial key points in a previous image frame and a confidence level corresponding to the previous image frame. The method also includes calculating coordinates of facial key points in the current image frame according to the coordinates of the facial key points in the previous image frame when the confidence level is higher than a preset threshold; and performing multi-face recognition on the current image frame according to the coordinates of the facial key points in the current image frame. The method also includes calculating a confidence level of the coordinates of the facial key points in the current image frame, and returning to process a next frame until recognition on all image frames is completed.