A probabilistic model of face mapping with local transformations and its application to person recognition

Abstract

Abstract-This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations. The proposed system of local transformations and neighboring constraints is embedded within the probabilistic framework of a two-dimensional hidden Markov model. More specifically, we model two types of intraclass variabilities involving variations in facial expressions and illumination, respectively. The performance of the resulting method is assessed on a large data set consisting of four face databases. In particular, it is shown to outperform a leading approach to face recognition, namely, the Bayesian intra/extrapersonal classifier.

Recognizing Rotated Faces From Frontal and Side Views: An Approach Toward Effective Use of Mugshot Databases

“…For face-recognition techniques under illumination variations and expression variations, readers can refer to [2], [12], [20], [21], [35], [38], [45], [46], [63], as well as [8], [40], [41], [53], [56], and [59], respectively. The rest of this paper is organized as follows.…”

A Comprehensive Review of Significant Researches on Face Recognition Based on Various Conditions

“…Since faces of varied persons contribute to global shape features, whereas face images of a single person is exposed to significant differences, which might overcome the measured inter-person variations, face recognition appears to have aspiring challenges. The facts that assimilate facial expressions, illumination conditions, pose, presence or absence of eyeglasses and facial hair cause such lack of correspondence [12], [13]. Output, simplicity, and non-invasiveness are dynamic advantages of face recognition as a biometric.…”

An Efficient Face Recognition System based on the Hybridization of Pose Invariant and Illumination Process

“…Face recognition seems to encompass aspire challenges, while faces of different persons throw in to global shape individuality, whereas face images of a single person is subject to considerable variations, which might overcome the measured inter-person differences Such variation is owing to the facts to incorporate facial expressions, illumination conditions, pose, presence or absence of eyeglasses and facial hair, occlusion, and aging [7,8]. The essential consequence of face recognition as a biometric is its throughput, expediency and noninvasiveness.…”

“…The changes stimulated by illumination are frequently larger than the differences between individuals, causing systems based directly on comparing images to misclassify input images. There are four main categories of looms for handling variable illumination: (1) extraction of illumination constant aspects (2) renovation of images with uneven illuminations to a canonical representation (3) modeling the illumination variations (4) exploitation of some 3D face models whose facial shapes and albedos are obtained in advance [7].…”