Two-dimensional discriminant multi-manifolds locality preserving projection for facial expression recognition

• Published in: 2015 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 2065 - 2068
• Main Authors: Ning Zheng, Xin Guo, Lin Qi, Ling Guan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Diversity reception; Emotion recognition; Face recognition; facial expression recognition; Feature extraction; manifold learning; Manifolds; Training; two-dimensional multi-manifolds discriminant locality preserving projection (2D-DMLPP)
• ISSN: 0271-4302
• DOI: 10.1109/ISCAS.2015.7169084

In this paper, we assume that samples of different expressions reside on different manifolds and propose a novel human emotion recognition framework named two-dimensional discriminant multi-manifolds locality preserving projection (2D-DMLPP). 2D-DMLPP focuses on salient regions which reflect the significant variation from facial expression images so that it can learn an expression-specific model from salient patches rather than that of subject-specific. Furthermore, conventional manifold learning methods ignore the variation among nearby samples from the same class, leading to serious overfitting. We construct three adjacency graphs to model the margin and information, including diversity and similarity of salient patches from the same expression, and then incorporate the information and margin into dimensionality reduction function. Several experiments show that the proposed method significantly improves the recognition performance of facial expression recognition.