Emotion recognition from facial images with arbitrary views
Xiaohua Huang, Guoying Zhao, Matti Pietikäinen · 2013
Facial expression recognition has been predominantly utilized to analyze the emotional status of human beings. In practice nearly frontal-view facial images may not be available. Therefore, a desirable property of facial expression recognition would allow the user to have any head pose. Some methods on non-frontal-view facial images were recently proposed to recognize the facial expressions by building discriminative subspace in specific views. We argue that this kind of approach ignores (1) the discrimination of inter-class samples with the same view label and (2) the closeness of intra-class samples with all view labels. This paper proposes a new method to recognize arbitrary-view facial expressions by using discriminative neighborhood preserving embedding and multi-view concepts. It first captures the discriminative property of inter-class samples. In addition, it explores the closeness of intra-class samples with arbitrary view in a low-dimensional subspace. Experimental results on BU-3DFE and Multi-PIE databases show that our approach achieves promising results for recognizing facial expressions with arbitrary views.