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EURASIP Journal on Image and Video Processing

, 2014:51

First Online: 25 November 2014Received: 01 April 2014Accepted: 06 November 2014

Abstract

In this paper, we introduce a fully automatic framework for 3D face recognition under expression variation. For 3D data preprocessing, an improved nose detection method is presented. The small pose is corrected at the same time. A new facial expression processing method which is based on sparse representation is proposed subsequently. As a result, this framework enhances the recognition rate because facial expression is the biggest obstacle for 3D face recognition. Then, the facial representation, which is based on the dual-tree complex wavelet transform DT-CWT, is extracted from depth images. It contains the facial information and six subregions’ information. Recognition is achieved by linear discriminant analysis LDA and nearest neighbor classifier. We have performed different experiments on the Face Recognition Grand Challenge database and Bosphorus database. It achieves the verification rate of 98.86% on the all vs. all experiment at 0.1% false acceptance rate FAR in the Face Recognition Grand Challenge FRGC and 95.03% verification rate on nearly frontal faces with expression changes and occlusions in the Bosphorus database.

KeywordsDual-tree complex wavelet transform 3D face recognition Sparse representation Linear discriminant analysis Electronic supplementary materialThe online version of this article doi:10.1186-1687-5281-2014-51 contains supplementary material, which is available to authorized users.

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Autor: Xueqiao Wang - Qiuqi Ruan - Yi Jin - Gaoyun An

Fuente: https://link.springer.com/







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