Name
Abstract | ||
|---|---|---|
Facial expression recognition (FER) using 2D images has been rapidly developed in the past decade. However, existing 2D-based FER methods seldom consider the impact of identity factors, and do not utilize shape features which have been proven to be effective complement to texture features. Built upon latest 3D face reconstruction methods, this paper proposes to generate expression-induced shape deformation map (ESDM) from the 3D face reconstructed from the input 2D face image, and then extract shape feature from ESDM by using a deep network. The shape feature is then combined with the texture feature on the input 2D face image, resulting in a fused feature, based on which the expression of the input 2D face image is recognized by using a softmax classifier. Evaluation experiments on BU-3DFE, MMI and CK+ databases show that our proposed shape feature effectively improves the 2D-based FER accuracy, and our method using the fused feature achieves state-of-the-art accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-31456-9_21 | BIOMETRIC RECOGNITION (CCBR 2019) |
Keywords | DocType | Volume |
Facial expression recognition,Expression-induced shape deformation map,3D face reconstruction | Conference | 11818 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Li | 1 | 11 | 3.90 |
| Zhengxi Liu | 2 | 0 | 1.01 |
| Qijun Zhao | 3 | 419 | 38.37 |