Face Recognition under Varying Lighting Based on the Probabilistic Model of Gabor Phase

This paper present a novel method for robust illumination-tolerant face recognition based on the Gabor phase and a probabilistic similarity measure. Invited by the work in Eigenphases [1] by using the phase spectrum of face images, we use the phase information of the multi-resolution and multi-orientation Gabor filters. We show that the Gabor phase has more discriminative information and it is tolerate to illumination variations. Then we use a probabilistic similarity measure based on a Bayesian (MAP) analysis of the difference between the Gabor phases of two face images. We train the model using some images in the illumination subset of CMU-PIE database and test on the other images of CMU-PIE database and the Yale B database and get comparative results. 1

Face recognition with harmonic delighting

Evaluations of the state-of-the-art of both academic face recognition algorithms and commercial systems have shown that performance of most current technologies degrades due to the variations of illumination. We propose a novel technique for face recognition under generic illumination in this paper, namely, calibrating an input face image to an image under canonical illumination, named as face de-lighting, to reduce the negative effect of non-ideal illumination in the original image. The canonical illumination is defined as an illumination environment in which light is constant in every direction. Inspired by the low dimension effect of light on Lambertian surface and the compact representation of the canonical illumination in spherical frequency space (only the DC component needed), face de-lighting is achieved with spherical harmonics. Experiments show the effectiveness of the proposed method in both lighting estimation and face recognition. 1

FACE RECOGNITION UNDER VARYING LIGHTING BASED ON DERIVATES OF LOG IMAGE

This paper considers the problem of recognizing faces under varying illuminations. First, we investigate the statistics of the derivative of the irradiance images (log) of human face and find that the distribution is very sparse. Based on this observation, we propose an illumination insensitive similarity measure based on the min operator of the derivatives of two images. Our experiments on the CMU-PIE database have shown that the proposed method improves the performance of a face recognition system when the probes are collected under varying lighting conditions. 1

Illumination Invariant Shot Boundary Detection

Abstract. Illumination variation poses a serious problem in video shot detection. It causes false cuts in many shot detection algorithms. A new illumination invariant measure metric is proposed in this paper. The metric is based on the assumption: the outputs of derivative filters to log-illumination are sparse. Thus the outputs of derivative filters to log-image are mainly caused by the scene itself. If the total output is larger than a threshold, it can be declared as a scene change or a shot boundary. Although this metric can detect gradual transitions as well as cuts, it is applied as a post-process procedure for a cut candidate because an illumination change is usually declared as a false cut.