Hybrid Deep Learning for Face Verification
文献类型：会议论文
| 作者 | Yi Sun; 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 | This paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification in wild conditions. A key contribution of this work is to directly learn relational visual features, which indicate identity similarities, from raw pixels of face pairs with a hybrid deep network. The deep ConvNets in our model mimic the primary visual cortex to jointly extract local relational visual features from two face images compared with the learned filter pairs. These relational features are further processed through multiple layers to extract high-level and global features. Multiple groups of ConvNets are constructed in order to achieve robustness and characterize face similarities from different aspects. The top-layer RBM performs inference from complementary high-level features extracted from different ConvNet groups with a two-level average pooling hierarchy. The entire hybrid deep network is jointly fine-tuned to optimize for the task of face verification. Our model achieves competitive face verification performance on the LFW dataset. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4500]  |
| 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 | Yi Sun,Xiaogang Wang,Xiaoou Tang. Hybrid Deep Learning for Face Verification[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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