|Place of Conferral||北京|
|Keyword||人脸活体检测 人脸防伪 多模态 卷积神经网络 虚拟生成|
Recently, face recognition techniques develop rapidly and have been deployed in many real applications. However, various presentation attacks threaten existing face recognition systems. The security has become a bottleneck restricting the applications of face recognition systems. Nowadays, the threat from the spoofing attacks, such as face images, videos or 3D masks of legitimate users, has been realized and the face presentation attack detection (PAD) has attracted a lot of attention.
This thesis summarizes previous methods of face presentation attack detection and provides a systematic and deep analysis for the issue of face presentation attack detection. To make face presentation attack detection systems more eﬀective, the author makes use of near-infrared (NIR) images and depth images to detect spoofing faces. The main contributions of this work are listed as follows:
(1) This thesis proposes a face presentation attack detection method based on depth images. Since genuine face and printed photo have different 3D structures, the use of 3D information is straightforward and significantly benefits face spoofing detection systems. The SVM-based method and the convolutional neural network-based method are proposed to utilize depth images for face spoofing detection and achieve good performance.
(2) This thesis collects a multi-modality 3D mask face anti-spoofing database and presents three multi-modal fusion methods as baseline algorithms to effectively merge the involved 2 modalities. Most of the existing databases focus on the 2D attacks, including photo and video attacks. The only two public 3D mask face anti-spoofing databases are relatively small. The collected database contains 920 videos of 67 genuine subjects wearing 48 kinds of 3D masks, captured in visual (VIS) and near-infrared (NIR) modalities. Furthermore, the author also builds three protocols and examine the performance of multi-modal fusion methods for face spoofing detection. The author hopes this database would help to promote the development of 3D face mask presentation attack detection techniques.
(3) This thesis proposes a virtual image synthesis method, which is able to generate bent and out-of-plane spoof samples so that large scale spoof data can be generated for training deep neural networks to boost the anti-spoofing performance. Since acquiring spoof data is very expensive because of the live faces should be re-printed and re-captured in many views, the proposed method could synthesize virtual spoof data in 3D space to alleviate this problem.
In summary, this thesis studies face liveness detection techniques based on different modalities. The face spoofing detection methods based on depth image, VIS and NIR modalities are proposed and evaluated. A virtual face synthesis method is also proposed to alleviate the over-fitting problem for deep learning based methods.
|肖金川. 多种模态下的人脸活体检测技术研究[D]. 北京. 中国科学院大学,2019.|
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