An evaluation of Gabor orientation as a feature for face recognition

Identifying a reliable feature is extremely important for all pattern recognition systems. The Gabor filter, which simultaneously captures spatial and frequency information, has been a vital component in numerous systems as a feature extractor. This filter produces three basic features - magnitude, phase, and orientation. Most face recognition methods based on Gabor filters use either the magnitude feature alone or a combination of the phase and magnitude features; very few are purely based on the phase feature, and the orientation feature is ignored. The aim of this paper is to evaluate these three basic features for face recognition using the FERET and AR face databases. The results show that the orientation feature is the most robust and distinctive feature, 20% and over 10% more accurate than the phase and magnitude features, respectively.