Deep Learning Identity-Preserving Face Space
文献类型：会议论文
| 作者 | Zhenyao Zhu; Ping Luo; Xiaogang Wang; Xiaoou Tang |
| 出版日期 | 2013 |
| 会议名称 | 2013 14th IEEE International Conference on Computer Vision, ICCV 2013 |
| 会议地点 | Sydney, NSW, Australia |
| 英文摘要 | Face recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learning based face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminative ness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the reconstruction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4499]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Zhenyao Zhu,Ping Luo,Xiaogang Wang,et al. Deep Learning Identity-Preserving Face Space[C]. 见:2013 14th IEEE International Conference on Computer Vision, ICCV 2013. Sydney, NSW, Australia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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