An improved Bayesian face recognition algorithm in PCA subspace

Through modeling the difference between two face images by three components, intrinsic difference (I), transformation difference (T), and random noise (N), we show that the Bayesian algorithm can successfully separate the main disturbing component, T, from the discriminating component, I, however, at a cost of magnified noise, N. To control the noise, we apply PCA on the original image, then carry out the Bayesian analysis in the reduced PCA space. The new method is shown to be more effective than the standard Bayesian algorithm in experiments using 2000+ face images from the Feret database.