Facial Emotion Recognition Using NLPCA and SVM

The aim of this present work is to achieve better accuracy of facial emotion recognition and classification with limited training samples under varying illumination. A method (involving two versions) for achieving high accuracy with limited samples is proposed. Global and local features of facial expression images were extracted using Haar Wavelet Transform (HWT) and Gabor wavelets respectively. Dimensionalities of extracted features are reduced using Nonlinear principal component analysis (NLPCA). Concatenated and weighted fusion techniques have been employed for fusing the global and local features. To recognize and classify six emotions (joy, surprise, fear, disgust, anger, and sadness) from facial expressions a Support Vector Machine was used. The proposed method is evaluated on Extended Cohn-Kanade dataset. The average recognition rates of 97.3 % and 98 % are achieved with the two versions of the proposed method, providing better recognition accuracy compared with the existing methods.