http://dx.doi.org/10.1016/j.patcog.2014.03.027">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Face hallucination with imprecise-alignment using iterative sparse representation

Language

English

Abstract

Existing face hallucination methods assume that the face images are well-aligned. However, in practice, given a low-resolution face image, it is very difficult to perform precise alignment. As a result, the quality of the super-resolved image is degraded dramatically. In this paper, we propose a near frontal-view face hallucination method which is robust to face image mis-alignment. Based on the discriminative nature of sparse representation, we propose a global face sparse representation model that can reconstruct images with mis-alignment variations. We further propose an iterative method combining the global sparse representation and the local linear regression using the Expectation Maximization (EM) algorithm, in which the face hallucination is converted into a parameter estimation problem with incomplete data. Since the proposed algorithm is independent of the face similarity resulting from precise alignment, the proposed algorithm is robust to mis-alignment. In addition, the proposed iterative manner not only combines the merits of the global and local face hallucination, but also provides a convenient way to integrate different strategies to handle the mis-alignment problem. Experimental results show that the proposed method achieves better performance than existing methods, especially for mis-aligned face images. © 2014 Elsevier Ltd.

Keywords

EM algorithm, Face hallucination, Mis-alignment, Sparse representation

Publication Date

2014

Source Publication Title

Pattern Recognitioin

Volume

47

Issue

10

Start Page

3327

End Page

3342

Publisher

Elsevier

ISSN (print)

00313203

ISSN (electronic)

18735142

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