ECCV 2014 - LNCS 8689-8695

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

Jiwen Lu¹, Gang Wang^{1, 2}, Weihong Deng³, and Pierre Moulin^{1, 4}

¹Advanced Digital Sciences Center, Singapore

²Nanyang Technological University, Singapore

³Beijing University of Posts and Telecommunications, Beijing, China

⁴University of Illinois at Urbana-Champaign, IL, USA

Abstract. In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a face image set captured from different poses, illuminations, expressions and resolutions. While several feature learning and dictionary learning methods have been proposed for image set based face recognition in recent years, most of them learn the features and dictionaries separately, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face images so that discriminative information can be jointly exploited. Extensive experimental results on four widely used face datasets show that our method achieves better performance than state-of-the-art image set based face recognition methods.

Keywords: Face recognition, image set, feature learning, dictionary learning, simultaneous learning

LNCS 8689, p. 265 ff.

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