Emotion Recognition from Faces Using Effective Features Extraction Method

With the rapid development and requirement of application with Artificial Intelligent (AI) technologies, the researches related to human-computer interaction are always active and the emotional status of the users is very essential for most of the environment. Facial Emotion Recognition, FER is one of the important visual information providers for the AI systems. This paper proposes a FER system using an effective feature extraction methodology and classification technologies. Local features of the face are more effective features for recognition and Scale Invariant Feature Transform, SIFT can give a better representation of the face. The bag of the visual word (BOVW) is the good encoding method and the advancement of that model Vector of Locally Aggregate Descriptor, VLAD provides the better encoder for SIFT features and used these benefits for feature extraction environments. The power of SVM includes unknown class recognition problems and this advantage is used for classification. This system used the standard basement JAFEE dataset to measure the success of the proposed methods and prepared to compare with other systems. The proposed system achieves the better result when it compared with some of the other previous systems because of the combination of effective feature extraction and encoding method.