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Classification of face images using local iterated function systems
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.
History
Journal
Machine vision and applicationsVolume
19Issue
4Pagination
223 - 248Publisher
Springer - VerlagLocation
Berlin, GermanyPublisher DOI
ISSN
0932-8092eISSN
1432-1769Language
engNotes
Published online: 18 August 2007Publication classification
C1 Refereed article in a scholarly journalCopyright notice
2007, Springer-VerlagUsage metrics
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Keywords
collage theoremface imagesfractalsimage variationslocal iterated function systemsrecognitionScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, CyberneticsEngineering, Electrical & ElectronicComputer ScienceEngineeringMODELSPCAArtificial Intelligence and Image ProcessingComputation Theory and Mathematics
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