Fast face recognition method using high order autocorrelations
F. Goudail, E. Lange, Takashi Iwamoto, Kazuo Kyuma, N. Otsu · 2005
We describe both the implementation and the evaluation of a face recognition method. The feature extraction algorithm is based on the computation of local autocorrelation coefficients. The main characteristics of these coefficients are simplicity of computation and a built-in translational invariance, which allows the system to respond in real time. The classification is realized by conventional methods; namely least square discriminant mapping and linear discriminant analysis, which may be implemented with hardware neural networks. We tested the system on a database of 11600 images of 116 persons. The simulations show peak recognition rates of up to 98% and a satisfactory rejection vs. recognition ratio.