Face Recognition across Pose: A Review

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Title Face Recognition across Pose: A Review
Author Zhang, Paul; Gao, Yongsheng
Journal Name Pattern Recognition
Year Published 2009
Place of publication Netherlands
Publisher Elsevier B.V.
Abstract One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilised. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalising different tactics in handling pose variations and evaluating their performances, several promising directions for future research have been suggested.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.patcog.2009.04.017
Copyright Statement Copyright 2009 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 42
Issue Number 11
Page from 2876
Page to 2896
ISSN 0031-3203
Date Accessioned 2010-02-23
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Image Processing; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/30193
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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