IEEE Trans Pattern Anal Mach Intell - A Markov Random Field Groupwise Registration Framework for Face Recognition.

Tópicos

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Resumo

In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.

Resumo Limpo

paper propos new framework tackl face recognit problem face recognit problem formul groupwis deform imag registr featur match problem main contribut propos method lie follow aspect pixel facial imag repres anatom signatur obtain correspond salient scale local region determin surviv exponenti entropi see inform theoret measur base anatom signatur calcul pixel novel markov random field base groupwis registr framework propos formul face recognit problem featur guid deform imag registr problem similar differ facial imag measur nonlinear riemannian manifold base deform transform propos method suffer generaliz problem exist common learn base algorithm propos method extens evalu four public avail databas feret caspealr frgc ver lfw also compar sever stateoftheart face recognit approach experiment result demonstr propos method consist achiev highest recognit rate among method comparison

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