A Deep Sum-Product Architecture for Robust Facial Attributes Analysis
文献类型：会议论文
| 作者 | Ping Luo; 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 | Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their estimation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Second, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the region's localization and classification. Experimental results on a large data set with 22,400 images show the effectiveness of the proposed approach. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4498]  |
| 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 | Ping Luo,Xiaogang Wang,Xiaoou Tang. A Deep Sum-Product Architecture for Robust Facial Attributes Analysis[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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