Detecting Faces Using Inside Cascaded Contextual CNN
文献类型：会议论文
| 作者 | Kaipeng Zhang; Zhanpeng Zhang; Hao Wang; Zhifeng Li; Yu Qiao; Wei Liu |
| 出版日期 | 2017 |
| 会议地点 | 意大利威尼斯 |
| 英文摘要 | Abstract Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild. Classical CNN-based face detection methods simply stack successive layers of filters where an input sample should pass through all layers before reaching a face/non-face de- cision. Inspired by the fact that for face detection, filters in deeper layers can discriminate between difficult face/non- face samples while those in shallower layers can efficiently reject simple non-face samples, we propose Inside Cascad- ed Structure that introduces face/non-face classifiers at d- ifferent layers within the same CNN. In the training phase, we propose data routing mechanism which enables differ- ent layers to be trained by different types of samples, and thus deeper layers can focus on handling more difficult sam- ples compared with traditional architecture. In addition, we introduce a two-stream contextual CNN architecture that leverages body part information adaptively to enhance face detection. Extensive experiments on the challenging FD- DB and WIDER FACE benchmarks demonstrate that our method achieves competitive accuracy to the state-of-the- art techniques while keeps real time performance. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11762]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Kaipeng Zhang,Zhanpeng Zhang,Hao Wang,et al. Detecting Faces Using Inside Cascaded Contextual CNN[C]. 见:. 意大利威尼斯. |
入库方式： OAI收割
来源：深圳先进技术研究院
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