Human face extraction and recognition using radial basis function networks
Partial face images, e.g., eyes, nose, and ear images are significant for face recognition. In this paper, we present a method for partial face extraction and recognition based on Radial Basis Function (RBF) networks. Focus has been centered on using ear images because they are not influenced by facial expression, and the influences of aging are negligible. Original human side face image with 320 x 240 pixels is input, and then the RBF network locates the ear and extracts it with a 200 x 120 pixel image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is proposed. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We built a database that contains 600 side face images, from 100 people, to test the method and the results of both extraction and recognition are satisfied.
radial basis function network face recognition dynamic radius