Partial Face Extraction and Recognition Using Radial Basis Function Networks

work, applies a nonlinear transformation from the input space to the hidden space. The output layer Partial face images, e.g.1 eyes, nose, and ear supplies the response of the network to the activaimages are significant for face recognition. In this tion pattern. 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 320x 240 pixels is input, and then the RBF network locates the ear and extracts it with a 200 x 120 pixels image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is introduced. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We build a database that contains 1...

• Type: Conference
• Year: 2000
• Where: MVA
• Authors: Nan He, Kiminori Sato, Yukitoshi Takahashi
• Keywords: Face Images, MVA 2000, MVA 2007, Rbf Functions, RBF Network