Gender and Smile Classification using Deep Convolutional Neural Networks
文献类型：会议论文
| 作者 | Kaipeng Zhang; Lianzhi Tan; Zhifeng Li; Yu Qiao |
| 出版日期 | 2016 |
| 会议名称 | CVPR Workshop 2016 |
| 会议地点 | 美国 |
| 英文摘要 | Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute- specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10028]  |
| 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 | Kaipeng Zhang,Lianzhi Tan,Zhifeng Li,et al. Gender and Smile Classification using Deep Convolutional Neural Networks[C]. 见:CVPR Workshop 2016. 美国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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