A theoretical analysis of generalized spectroface for face recognition

Spectroface is a face representation method using a 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, have been proposed and successfully applied to face recognition. The paper reports a generalized spectroface in which we prove that any continuous and compact supported low pass filter can be used to replace the wavelet transform. This feature provides high flexibility of spectroface representation. It is also proved that the generalized spectroface is translation invariant. A simple but effective feature selection algorithm is also proposed for the generalized spectroface in which recognition is further increased. Three standard databases from Yale University, Olivetti 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 on a Pentium II 300 MHz computer using Matlab is around 3 seconds.