Abstract: Facial emotion plays a vital role for human interactive communication and also used in numerous real applications. Facial expression identification from frontal still images has in recent times become a hopeful investigation area. Their applications include human-computer interface, human emotion examination robot control, driver state surveillance and medical fields. This paper aims to develop emotion classification scheme to identify seven dissimilar facial emotions, such as surprise, sad, neutral, happy, fear, disgust and anger by using JAFFE database. Two different approaches of feature selection and extraction have been used for generation of optimal feature vector. LBP and 2D- DCT coefficients are employed in addition to image statistics, texture and entropy parameters. In order to reduce the high dimensionality of the inputs, the principal component analysis has been used and significant reduction in the input-dimensionality has been achieved. The single hidden layer feedforward neural network has been used as a classifier in order to classify different emotions from frontal facial images. Three learning algorithms such as resilient backpropagation, scaled conjugate gradient and gradient descent algorithm with momentum and adaptive learning rate have been compared. It has been observed that LBP based feature vector and neural network with gradient descent algorithm with momentum and adaptive learning rate delivers the best classification performance.
Keywords: Facial Emotions, 2D DCT, LBP, PCA, neural network, Classifier.