Name
Title | ||
|---|---|---|
Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition. |
Abstract | ||
|---|---|---|
In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data. Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes. More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1812.07067 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Cai | 1 | 57 | 4.77 |
| Zibo Meng | 2 | 248 | 13.60 |
| KHAN, AHMED-SHEHAB | 3 | 31 | 3.47 |
| Zhiyuan Li | 4 | 1 | 5.76 |
| James O'Reilly | 5 | 22 | 3.02 |
| Yan Tong | 6 | 409 | 21.36 |