IEEE Trans Image Process - Pose-invariant face recognition using Markov random fields.

Tópicos

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Resumo

One of the key challenges for current face recognition techniques is how to handle pose variations between the probe and gallery face images. In this paper, we present a method for reconstructing the virtual frontal view from a given nonfrontal face image using Markov random fields (MRFs) and an efficient variant of the belief propagation algorithm. In the proposed approach, the input face image is divided into a grid of overlapping patches, and a globally optimal set of local warps is estimated to synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it with images from a training database of frontal faces. The alignments are performed efficiently in the Fourier domain using an extension of the Lucas-Kanade algorithm that can handle illumination variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem using an MRF. The reconstructed frontal face image can then be used with any face recognition technique. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, we also present an algorithm for classifying whether a given face image is at a frontal or nonfrontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach.

Resumo Limpo

one key challeng current face recognit techniqu handl pose variat probe galleri face imag paper present method reconstruct virtual frontal view given nonfront face imag use markov random field mrfs effici variant belief propag algorithm propos approach input face imag divid grid overlap patch global optim set local warp estim synthes patch frontal view set possibl warp patch obtain align imag train databas frontal face align perform effici fourier domain use extens lucaskanad algorithm can handl illumin variat problem find optim warp formul discret label problem use mrf reconstruct frontal face imag can use face recognit techniqu two main advantag method requir manual select facial landmark head pose estim order improv perform pose normal method face recognit also present algorithm classifi whether given face imag frontal nonfront pose experiment result differ dataset present demonstr effect propos approach

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