Pursuing face identity from view-specific representation to view-invariant representation
文献类型：会议论文
| 作者 | Zhang, Ting1 ; Dong, Qiulei1,2,3; Hu, Zhanyi1,2,3
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| 出版日期 | 2016 |
| 会议日期 | September 25-28, 2016 |
| 会议地点 | Phoenix, Arizona, USA |
| 关键词 | Face Identity |
| 英文摘要 | How 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. |
| 会议录 | Proceedings of International Conference on Image Processing
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12446]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
| 通讯作者 | Dong, Qiulei |
| 作者单位 | 1.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 |
| 推荐引用方式 GB/T 7714 | Zhang, Ting,Dong, Qiulei,Hu, Zhanyi. Pursuing face identity from view-specific representation to view-invariant representation[C]. 见:. Phoenix, Arizona, USA. September 25-28, 2016. |
入库方式： OAI收割
来源：自动化研究所
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