Face recognition using optimal non-orthogonal wavelet basis evaluated by information complexity

Detecting and recognizing face images automatically is a difficult task due to the variability of illumination, presentation angle, face expression and other common problems of machine vision. We represent face images as combinations of 2-D Gabor wavelet basis which are non-orthogonal. A genetic algorithm (GA) is used to find an optimal basis derived from a combination of frequencies and orientation angles in the 2-D Gabor wavelet transform. Instead of using the widely used within and between class scatter evaluation as the fitness function in GA, we use entropy to measure the information complexity of the wavelet transform. Compared to the well-known "eigenface" algorithm which represents face images based on an orthogonal basis, this Gabor wavelet representation with optimal basis can provide a more accurate and efficient projection scheme and therefore a better classification result.