Human Face Expression Recognition

Abstract — A real-time face detection algorithm for locating faces in images and videos is a one of the main research area in computer vision. Here, we propose an efficient method for emotion recognition from facial expressions in static color images containing the frontal view of the human face. Our goal is to categorize the facial expression in the given image into four basic emotional states – Happy, Sad, Neutral and Surprise. Our method consists of three steps, namely face detection and localization, facial feature extraction and emotion recognition. First, face detection is performed using a novel skin-color extraction and extraction of location of facial component features such as the eye and the mouth analysis by using a knowledge based approach and projection. Next, the extraction of facial features distance is performed by employing an iterative search algorithm, on the edge information of the localized face region in gray scale. Finally, emotion recognition is performed by giving the extracted eleven facial features as input to different classifiers like PCA with Fuzzy C-means, Rule based classification and feed-forward neural network trained by back-propagation and analyzed based on accuracy