This paper presents a novel algorithm for the extraction of the eye and mouth (facial features) fields from 2D gray level images. Eigenfeatures are derived from the eigenvalues and eigenvectors of the binary edge data set constructed from eye and mouth fields. Such eigenfeatures are ideal features for finely locating fields efficiently. The eigenfeatures are extracted from a set of the positive and negative training samples for facial features and are used to train a multilayer perceptron (MLP) whose output indicates the degree to which a particular image window contains the eyes or the mouth within itself. An ensemble network consisting of a multitude of independent MLPs was used to enhance the generalization performance of a single MLP. It was experimentally verified that the proposed algorithm is robust against facial size and even slight variations of the pose. [References: 16]
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