Multiresolution face recognition

In this paper the contribution of multiresolution analysis to the face recognition performance is examined. We refer to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, feature or decision level improves the correct decision performance. In our proposed method, prior to the subspace projection operation like principal or independent component analysis, we employ multiresolution analysis to decompose the image into its subbands. Our aim is to search for the subbands that are insensitive to the variations in expression and in illumination. The classification performance is improved by fusing the information coming from the subbands that attain individually high correct recognition rates. The proposed algorithm is tested on face images that differ in expression or illumination separately, obtained from CMU PIE, FERET and Yale databases. Significant performance gains are attained, especially against illumination perturbations.