Deep Learning Face Representation by Joint Identification-Verification
文献类型：会议论文
| 作者 | Yi Sun; Xiaogang Wang; Xiaoou Tang |
| 出版日期 | 2014 |
| 会议名称 | The 28 Annual Conference on Neural Information Processing Systems (NIPS) |
| 会议地点 | 加拿大 |
| 英文摘要 | The key challenge of face recognition is to develop effective feature repre- sentations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset [11], 99.15% face verification accuracy is achieved. Compared with the best deep learning result [21] on LFW, the error rate has been significantly reduced by 67%. |
| 收录类别 | 其他 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5516]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Yi Sun,Xiaogang Wang,Xiaoou Tang. Deep Learning Face Representation by Joint Identification-Verification[C]. 见:The 28 Annual Conference on Neural Information Processing Systems (NIPS). 加拿大. |
入库方式： OAI收割
来源：深圳先进技术研究院
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