Beijing Kuangshi Technology Co., Ltd.

Pinhole (beijing) Technology Co., Ltd.

Kai Jia

Qi Yin

Haoqiang Fan

Zhimin Cao

Tao He

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

There are provided a liveness detection method and device. The liveness detection method comprises: generating a random action instruction sequence including at least one random action instruction; sequentially sending a random action instruction in the random action instruction sequence; and determining whether the sequentially sent random action instruction in the random action instruction sequence is sequentially executed by a living body based on detection information of at least two sensors, wherein the at least two sensors comprise an image sensor and at least one non-image sensor; and determining that the liveness detection is succeeded if the sequentially sent random action instruction in the random action instruction sequence is sequentially executed by the living body. Accuracy of liveness detection can be improved by adopting the random action sequence and by combining images captured by the image sensor and information detected by the non-image sensor.

Method and apparatus for liveness detection

A disclosed face detection system (and method) is based on a structure of a convolutional neural network (CNN). One aspect concerns a method for automatically training a CNN for face detection. The training is performed such that balanced number of face images and non-face images are used for training by deriving additional face images from the face images. The training is also performed by adaptively changing a number of trainings of a stage according to automatic stopping criteria. Another aspect concerns a system for performing image detection by integrating data at different scales (i.e., different image extents) for better use of data in each scale. The system may include CNNs automatically trained using the method disclosed herein.

Face detection using machine learning

Disclosed are a method and a system for authenticating liveness face, and a computer program product thereof, which belong to a field of face recognition technique. The method for authenticating liveness face comprises: generating a sequence of action instructions randomly; and determining a success of the authentication of the liveness face when face actions are determined to be matched with the sequence of action instructions sequentially. The authentication of the liveness face can be performed based on fine head actions, so that a cost for authenticating the liveness face is decreased, an accuracy of authentication of the liveness face is increased.

Method and system for authenticating liveness face, and computer program product thereof

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.

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Beijing, China

Kai Jia