CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Pursuing face identity from view-specific representation to view-invariant representation
Zhang, Ting1; Dong, Qiulei1,2,3; Hu, Zhanyi1,2,3
2016
Conference NameIEEE International Conference on Image Processing
Source PublicationProceedings of International Conference on Image Processing
Conference DateSeptember 25-28, 2016
Conference PlacePhoenix, Arizona, USA
AbstractHow to learn view-invariant facial representations is an important task for view-invariant face recognition. The recent work [1] discovered that the brain of the macaque monkey has a face-processing network, where some neurons are view-specific. Motivated by this discovery, this paper proposes a deep convolutional learning model for face recognition, which explicitly enforces this view-specific mechanism for learning view-invariant facial representations. The proposed model consists of two concatenated modules: the first one is a convolutional neural network (CNN) for learning the corresponding viewing pose to the input face image; the second one consists of multiple CNNs, each of which learns the corresponding frontal image of an image under a specific viewing pose. This method is of low computational cost, and it can be well trained with a relatively small number of samples. The experimental results on the MultiPIE dataset demonstrate the effectiveness of our proposed convolutional model in contrast to three state-of-the-art works.
KeywordFace Identity
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12446
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorDong, Qiulei
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
3.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Ting,Dong, Qiulei,Hu, Zhanyi. Pursuing face identity from view-specific representation to view-invariant representation[C],2016.
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