Subject-independent emotion recognition from facial expressions using a Gabor feature RBF neural classifier trained with virtual samples generated by concurrent self-organizing maps
Victor-Emil Neagoe, Adrian-Dumitru Ciotec · International Conference on Signal Processing · 2011
The most expressive way humans display emotions is through facial expressions. This paper is dedicated to the challenging computer vision task of subject-independent emotion recognition from facial expressions. The original key idea of the proposed model is the increasing of the neural classifier training set size by adding samples generated with a system of Concurrent Self-Organizing Maps (CSOM). The model consists of the following main processing cascade: (a) Gabor Wavelet Filtering (GVF); (b) dimensionality reduction using Principal Component Analysis (PCA); (c) Radial Basis Function (RBF) neural classifier trained with virtual samples generated by CSOM system (VSG-CSOM). We have evaluated the above proposed model for person-independent facial expression recognition using JAFFE database. One obtains an average recognition score for the test set (leave-one subject out test method) of 69.70%. The advantage of using CSOM-VSG-RBF over a traditional RBF neural classifier means an improvement of recognition score with about 16% (from 53.44% for RBF to 69.70% for VSG-CSOM-RBF).