Automatic Emotion Detection using SVM-based Optimal Kernel Function

Facial expression is very essential in interpersonal relationships. Human-machine interaction is a recently emerging field in which the Facial Expression Recognition System (FERS) is a key aspect in determining the emotional state of a person. This paper proposes a unique way for automatically identifying face expressions from images using Principal Component Analysis (PCA). The proposed approach employs Haar features of the image to detect the face component. The PCA approach is then employed to extract the efficient Haar facial features, resulting in the Eigenvectors, which include dimensionality reduction. A SVM classifier with a kernel function is used to recognize common human emotions such as happiness, fear, sadness, etc. The experimental results demonstrate that the technique proposed in this research is more effective at classifying various expressions and has a higher recognition rate than traditional approaches.