Recognition of facial expression using Haar-like feature extraction method

In this paper, we attempted to recognize facial expression by using Haar-like feature extraction method. A set of luminance stickers were fixed on subject´s face and the subject is instructed to perform required facial expressions. At the same time, subject´s expressions were recorded in video. A set of 2D coordinate values are obtained by tracking the movements of the stickers in video using tracking software. We use Haar-like technique to extract the features. Six statistical features namely variance, standard deviation, mean, power, energy and entropy were derived from the approximation coefficients of Haar-like decomposition. These statistical features were used as an input to the neural network for classifying 8 facial expressions. The feature Variance offers better result compared to other statistical features.