A theoretical analysis of generalized spectroface for face recognition

Spectroface is a face representation method using wavelet transform and Fourier transform and has been proved to be invariant to translation, on-the-plane rotation and scale. Two types of spectrofaces, namely first order and second order spectrofaces, have been proposed and successfully applied for face recognition. This paper reports a generalized spectroface, in which, we prove that any continuous and compact supported low pass filters can be used to replace the wavelet transform. This feature provides a high flexibility of the spectroface representation. It is also proved that the generalized spectroface is translation invariant. A simple but efective feature selection algorithm is also proposed for the generalized spectroface in which the recognition is further increased. Three standard databases from Yale University, Olivette Research Laboratory and MIT, are used to evaluate the proposed method. The recognition accuracy is as high as 99.29%. If we consider the top three matches, the accuracy increases to 99.64%. The computational time in a Pentium II 300MHz computer using Matlab is around 3 seconds.