Gabor and Log-Gabor Wavelet for Face Recognition

In practice Gabor wavelet is often applied to extract relevant features from a facial image. This wavelet is constructed using filters of multiple scales and orientations. Based on Gabor's theory of communication, two methods are proposed to acquire initial features from 2D images that are Gabor wavelet and Log-Gabor wavelet. Theoretically the main difference between these two wavelets is Log-Gabor wavelet produces DC free filter responses, whereas Gabor filter responses retain DC components. This experimental study determines the characteristics of Gabor and Log-Gabor filters for face recognition. In the experiment, two sixth order data tensor are created; one containing the basic Gabor feature vectors and the other containing the basic Log-Gabor feature vectors. This study reveals the characteristics of the filter orientations for Gabor and Log-Gabor filters for face recognition. These two implementations show that the Gabor filter having orientation zero means oriented at 0 degree with respect to the aligned face has the highest discriminating ability, while Log-Gabor filter with orientation three means 45 degree has the highest discriminating ability. This result is consistent across three different frequencies (scales) used for this experiment. It is also observed that for both the wavelets, filters with low frequency have higher discriminating ability.