Beijing Kuangshi Technology Co., Ltd.

Pinhole (beijing) Technology Co., Ltd.

Megvii (beijing) Technology Co., Ltd.

Haoqiang Fan

Qi Yin

Zhimin Cao

Yuning Jiang

Kai Jia

Wei Sun

Erjin Zhou

Keqing Chen

The application provides a liveness detection method and a liveness detection system. The liveness detection method includes: obtaining first and second face image data of an object to be detected, and at least one of the first and the second face image data being a depth image; determining a first face region and a second face region, determining whether the first and the second face regions correspond to each other, and extracting, when it is determined that the first and the second face region corresponds to each other, a first and a second face image from the first and the second face region respectively; determining a first classification result for the extracted first face image and a second classification result for the extracted second face image; and determining, based on the first classification result and the second classification result, a detection result for the object to be detected.

Liveness detection method and liveness detection system

A facial expression detection method includes: obtaining an image to be processed; inputting the image to be processed into a neural network model to obtain a facial expression parameter of a target face in the image to be processed; and outputting the facial expression parameter of the target face for driving an object to be driven.

Facial expression detection method, device and system, facial expression driving method, device and system, and storage medium

A method for face liveness detection and a device for face liveness detection. The method for face liveness detection includes: performing an illumination liveness detection and obtaining an illumination liveness detection result; and determining whether or not a face to be verified passes the face liveness detection at least according to the illumination liveness detection result. Performing of the illumination liveness detection and obtaining of the illumination liveness detection result includes: acquiring a plurality of illumination images of the face to be verified, in which the plurality of illumination images are captured in a process of dynamically changing mode of illumination light irradiated on the face to be verified and are respectively corresponding to various modes of the illumination light; and obtaining the illumination liveness detection result according to a light reflection characteristic of the face to be verified in the plurality of illumination images.

Method and device for face liveness detection

The present invention relates to a living body detection method and living body detection system capable of realizing living body detection of a human body, and a computer program product. The living body detection method comprises: respectively using each light source of at least two light sources arranged at different positions to illuminate a face of an object to be detected; capturing a plurality of images of the face of the object to be detected that is illuminated by each light source; calculating a difference image between the plurality of images; and obtaining a detection value of the difference image, and determining that the object to be detected is a living body if the detection value is greater than a pre-determined threshold value.

Living body detection method, living body detection system, and computer program product

The present invention relates to a living body detection method, a living body detection system and a computer program product, which can realize living human body detection. The living body detection method includes: using a laser light source to irradiate the face of an object to be detected; capturing an image of the face of the object to be detected that is irradiated by the laser light source; calculating a light spot area of the image of the face of the object to be detected; and comparing the light spot area with a first predetermined area threshold value, and if the light spot area is greater than the first predetermined area threshold value, determining that the object to be detected is a living body.

Living body detection method, living body detection system and computer program product

The application provides a liveness detection method capable of implementing liveness detection on a human body, and a liveness detection system that employs the liveness detection method. The liveness detection method comprises: obtaining video data acquired via a video acquisition module; determining, based on the video data, a feature signal of an object to be detected; judging whether the feature signal meets a first predetermined condition, and if the feature signal meets the first predetermined condition, identifying that the object to be detected is a living body, wherein the feature signal is indicative of eye movement of the object to be detected.

Liveness detection method, liveness detection system, and liveness detection device

The application provides a liveness detection method capable of implementing liveness detection, and a liveness detection system that employs the liveness detection method. The liveness detection method comprises: irradiating an object to be detected with structured light; obtaining first facial image data of the object to be detected under irradiation of the structured light; determining, based on the first facial image data, a detection parameter that indicates a sub-surface scattering intensity of the structured light on a face of the object to be detected; and determining, based on the detection parameter and a predetermined parameter threshold, whether the object to be detected is a living body.

Liveness detection method, liveness detection system, and computer program product

There is disclosed an apparatus, system, method and computer program product for recognizing a face, the method comprising: emitting at least one group of structured light to a face to be recognized, successively; capturing a set of light-source-illuminated images of the face when the face is illuminated successively by each group of light of the at least one group of structured light; extracting a first set of features including a feature of each detection point in a set of detection points of the face based on the set of light-source-illuminated images; acquiring a second set of features including a feature of each detected point in a set of detected points of a face template; computing a similarity between the face and the face template based on the first set of features and the second set of features; and recognizing the face as being consistent with the face template if the similarity is larger than a threshold.

Apparatus, system, method and computer program product for recognizing face

A method for obtaining images for three dimension (3D) reconstruction comprises: controlling brightness of each of at least two light sources which are spatially separated from each other to be changed periodically, wherein, among the at least two light sources, there is at least one light source having a period of brightness change different from those of the other light source(s), or the periods of the brightness change of the at least two light sources are the same, but among the at least two light sources, there is at least one light source having a phase of the brightness change different from those of the other light source(s); and using at least three cameras having different spatial positions to capture the images for the 3D reconstruction, respectively, wherein, among the at least three cameras, there is at least one camera starting exposure at a different time from the other cameras.

Method and system for obtaining images for 3D reconstruction and method and system for 3D reconstruction

The application relates to a video detection method, a video detection system, and a computer program product, which can implement liveness detection for a human body. The video detection method comprises: obtaining video data acquired by a video data acquisition device; determining a to-be-detected object based on the video data; obtaining a to-be-detected signal corresponding to the to-be-detected object; and determining whether the to-be-detected signal is a liveness physiological signal, wherein the to-be-detected signal is a frequency domain signal corresponding to the video data of the to-be-detected object.

Video detection method, video detection system and computer program product

A liveness detection apparatus and a liveness detection method are provided. The liveness detection apparatus may comprise: a specific exhibiting device, for exhibiting a specific identification content; an image acquiring device, for acquiring image data of a target object to be recognized during the exhibition of the identification content; a processor, for determining whether there is a reflective region corresponding to the identification content in the acquired image data, determining a regional feature of the reflective region when there is the reflective region, to obtain a determination result, and recognizing whether the target object is a living body based on the determination result.

Liveness detection apparatus and liveness detection method

A deep learning framework jointly optimizes the compactness and discriminative ability of face representations. The compact representation can be as compact as 32 bits and still produce highly discriminative performance. In another aspect, based on the extreme compactness, traditional face analysis tasks (e.g. gender analysis) can be effectively solved by a Look-Up-Table approach given a large-scale face data set.

Compact face representation

The present invention overcomes the limitations of the prior art by performing facial landmark localization in a coarse-to-fine manner with a cascade of neural network levels, and enforcing geometric constraints for each of the neural network levels. In one approach, the neural network levels may be implemented with deep convolutional neural network. One aspect concerns a system for localizing landmarks on face images. The system includes an input for receiving a face image, and an output for presenting landmarks identified by the system. Neural network levels are coupled in a cascade from the input to the output for the system. Each neural network level produces an estimate of landmarks. The estimate of landmarks is more refined than an estimate of landmark of a previous neural network level.

Facial landmark localization using coarse-to-fine cascaded neural networks

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy to implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. A very easy-to-implement deep learning framework for face representation is presented. The framework bases on pyramid convolutional neural network (CNN). The pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation.

Growing community of inventors

Beijing, China

Haoqiang Fan