Vision Based Facial Expression Recognition Using Eigenfaces and Multi-SVM Classifier

Facial Expression Recognition (FER) has become one of the most popular areas of research in computer vision and biometrics authentication and it has achieved a lot of enthusiasm from researchers. The Vision based Facial Expression Recognition system intends to classify the facial expression of a given image. In this paper, the proposed system automatically classifies the facial expression. The system is composed of feature extraction and expression classification. In preprocessing, Hybrid filter (Median and Gabor) and Histogram Equalizations, is used to reduce noise and enhance images. Feature extraction is to extract feature vectors from face images using the Eigenfaces approach, based on Principal Component Analysis (PCA). To classify facial expression, extracted feature vectors are fed into a Multiclass Support Vector Machine (Multi-SVM) classifier. Experiments are performed on the standard dataset of the Japanese Female Facial Expression (JAFFE) and achieved 80% accuracy. The proposed system showed satisfying performance comparing with other methods and effects state-of-the-art performance on the JAFFE dataset.