Classification of face images using local iterated function systems |
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Authors: | A Z Kouzani |
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Affiliation: | (1) School of Engineering and Information Technology, Deakin University, Geelong, VIC, 3217, Australia |
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Abstract: | 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.
The author would like to thank the Australian Research Council (ARC) which supports this research with a Discovery Grant. |
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Keywords: | Collage theorem Face images Fractals Image variations Local iterated function systems Recognition |
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