Eigenphases vs eigenfaces

In this paper, we present a novel method for performing robust illumination-tolerant and partial face recognition that is based on modeling the phase spectrum of face images. We perform principal component analysis in the frequency domain on the phase spectrum of the face images and we show that this improves the recognition performance in the presence of illumination variations dramatically compared to normal eigenface method and other competing face recognition methods such as the illumination subspace method and fisherfaces. We show that this method is robustly even when presented with partial views of the test faces, without performing any pre-processing and without needing any a-priori knowledge of the type or part of face that is occluded or missing. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people showing the performance gain of our proposed method using a variety of training scenarios using as little as three training images per person. We also present partial face recognition results that obtained by synthetically blocking parts of the face of the test faces (even though training was performed on the full face images) showing gain in recognition accuracy of our proposed method.