Face Recognition using independent component analysis of GaborJet (GaborJet-ICA)

In this paper a new face recognition technique based on Independent Component Analysis of GaborJet (GaborJet-ICA) is proposed. Existing face recognition systems using Gabor wavelets convolve a whole face image with a set of 40 Gabor wavelets. We have derived Gabor feature vector from facial landmarks (fiducial points) known as GaborJets. We then transformed this GaborJet feature vector into the basis space of PCA and ICA. A series of experiments based on ORL database were then performed to evaluate the performance. During our experiments we varied number of subspace dimensions from 2 to 40 and numbers of independent components derived were in the range 1 to 200. As literature on PCA and ICA subject is contradictory, we compared the performance for GaborJet-PCA and GaborJet-ICA. The results show maximum accuracy of 82.25% and 84.5% for GaborJet-PCA and GaborJet-ICA respectively. This proves that the difference in performance between ICA and PCA is of 2.25%, which is insignificant.