Identification of human faces through texture-based feature recognition and neural network technology

A method is presented to infer the presence of a human face in an image through the identification of face-like textures. The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices, from which features can be calculated that can characterize a texture. The cascade-correlation neural network architecture is used for supervised classification of textures. The Kohonen self-organizing feature map shows the clustering of the different texture types. Classification performance is generally above 80%, which is sufficient to clearly outline a face in an image.< >