Human Facial Expression Recognition based on Principal Component Analysis and Artificial Neural Network

In recent years there has been a growing interest in improving aspects of the interaction between humans and computers. The facial expressions play an essential role in social interactions with other human beings. As indicated by Mehrabian [1], in face-to-face human communication only 7% of the communicative message is due to linguistic language, 38% is due to paralanguage, while 55% of it is transferred by facial expressions. Therefore, in order to facilitate a more friendly man–machine interface of new multimedia products, vision-based facial gesture analysis is being studied world wide in the last ten years. Numerous techniques have been proposed. We propose an algorithm for facial expression recognition, which can classify the given image into one of the seven basic facial expression categories (Happiness, Sadness, Fear, Surprise, Anger, Disgust and neutral). Our program has been tested using JAFFE Database, available at http://www.kasrl.org/jaffe.html using 113 images randomly selected for training and 100 images for testing, without any overlapping; we obtain a recognition rate equal to 92.12%.