Automatic extraction of eye and mouth fields from a face image using eigenfeatures and multilayer perceptrons
This paper presents a novel algorithm for the extraction of the eye and mouth (facial features) fields from 2-D gray-level face images. The fundamental philosophy is that eigenfeatures, derived from the eigenvalues and eigenvectors of the binary edge data set constructed from the eye and mouth fields, are very good features to locate these fields efficiently. The eigenfeatures extracted from the positive and negative training samples of the facial features are used to train a multilayer perceptron whose output indicates the degree to which a particular image window contains an eye or a mouth. It turns out that only a small number of frontal faces are sufficient to train the networks. Furthermore, they lend themselves to good generalization to non-frontal pose and even other people's faces. It has been experimentally verified that the proposed algorithm is robust against facial size and slight variations of pose.
eye and mouth fields eigenfeature multilayer perceptron facial feature positive (negative) sample