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Classification of face images using local iterated function systems

Kouzani, A. Z. 2008, Classification of face images using local iterated function systems, Machine vision and applications, vol. 19, no. 4, pp. 223-248, doi: 10.1007/s00138-007-0095-x.

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Title Classification of face images using local iterated function systems
Author(s) Kouzani, A. Z.ORCID iD for Kouzani, A. Z.
Journal name Machine vision and applications
Volume number 19
Issue number 4
Start page 223
End page 248
Total pages 26
Publisher Springer - Verlag
Place of publication Berlin, Germany
Publication date 2008-07
ISSN 0932-8092
Keyword(s) collage theorem
face images
image variations
local iterated function systems
Summary There has been an increasing interest in face recognition in recent years. Many recognition methods have been developed so far, some very encouraging. A key remaining issue is the existence of variations in the input face image. Today, methods exist that can handle specific image variations. But we are yet to see methods that can be used more effectively in unconstrained situations. This paper presents a method that can handle partial translation, rotation, or scale variations in the input face image. The principal is to automatically identify objects within images using their partial self-similarities. The paper presents two recognition methods which can be used to recognise objects within images. A face recognition system is then presented that is insensitive to limited translation, rotation, or scale variations in the input face image. The performance of the system is evaluated through four experiments. The results show that the system achieves higher recognition rates than those of a number of existing approaches.
Notes Published online: 18 August 2007
Language eng
DOI 10.1007/s00138-007-0095-x
Field of Research 090609 Signal Processing
Socio Economic Objective 810107 National Security
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag
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Document type: Journal Article
Collection: School of Engineering and Information Technology
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