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Individual stable space : an approach to face recognition under uncontrolled conditions

Geng, Xin, Zhou, Zhi-Hua and Smith-Miles, K. 2008, Individual stable space : an approach to face recognition under uncontrolled conditions, IEEE transactions on neural networks, vol. 19, no. 8, pp. 1354-1368.

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Title Individual stable space : an approach to face recognition under uncontrolled conditions
Author(s) Geng, Xin
Zhou, Zhi-Hua
Smith-Miles, K.
Journal name IEEE transactions on neural networks
Volume number 19
Issue number 8
Start page 1354
End page 1368
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2008
ISSN 1045-9227
1941-0093
Keyword(s) Face recognition (FR)
individual stable space (ISS)
machine learning
neural networks
pattern recognition
Summary There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a ldquodivide and conquerrdquo strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017604

Document type: Journal Article
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.