A deep sum-product architecture for robust facial attributes analysis
Refereed conference paper presented and published in conference proceedings


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. © 2013 IEEE.
著者Luo P., Wang X., Tang X.
會議名稱2013 14th IEEE International Conference on Computer Vision, ICCV 2013
會議地點Sydney, NSW
頁次2864 - 2871
關鍵詞attributes, deep learning, face recognition

上次更新時間 2020-23-11 於 01:21