Deep Face Attributes Recognition Using Spatial Transformer Network
文献类型：会议论文
| 作者 | Tan Lianzhi; Li Zhifeng; Qiao Yu |
| 出版日期 | 2016 |
| 会议名称 | ICIA2016 |
| 会议地点 | 荷兰阿姆斯特丹 |
| 英文摘要 | Face alignment is very crucial to the task of face attributes recognition. The performance of face attributes recognition would notably degrade if the fiducial points of the original face images are not precisely detected due to large lighting, pose and occlusion variations. In order to alleviate this problem, we propose a spatial transform based deep CNNs to improve the performance of face attributes recognition. In this approach, we first learn appropriate transformation parameters by a carefully designed spatial transformer network called LoNet to align the original face images, and then recognize the face attributes based on the aligned face images using a deep network called ClNet. To the best of our knowledge, this is the first attempt to use spatial transformer network in face attributes recognition task. Extensive experiments on two large and challenging databases (CelebA and LFWA) clearly demonstrate the effectiveness of the proposed approach over the current state-of-the-art. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10012]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Tan Lianzhi,Li Zhifeng,Qiao Yu. Deep Face Attributes Recognition Using Spatial Transformer Network[C]. 见:ICIA2016. 荷兰阿姆斯特丹. |
入库方式： OAI收割
来源：深圳先进技术研究院
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