Expression recognition from faces with varying pose and illumination conditions is a challenging research area with growing interest. In this paper, we develop a novel supervised super-vector encoding framework to learn discriminative image feature representations. The framework is then validated on the Multi-PIE and BU3D-FE databases for multi-view facial expression recognition. Extensive experiments show that our supervised framework gives significant improvement over the unsupervised counterpart and outperforms the state-of-the-arts.
- Face biometrics
- Facial expression recognition
- GMM learning
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence