Palm Recognition Using K-Mean Clustering With Geometrical and Texture Features
نویسنده
چکیده
This paper proposes two algorithms (k-mean clustering and back propagation) for palm recognition of an individual using geometrical and texture features. Palmprint recognition being one of the important aspects of biometric technology is one of the most reliable and successful identification methods. Thus palmprint recognition is a very interesting research area. In this study algorithm is proposed to extract geometrical features and principal lines as texture feature and result is compared in both cases. The database is taken from www.coep.org.in and http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm for processing. MATLAB with version 7.10.0.499 (R2010a) 32 bits will be used to implement the proposed work. In the case of k-mean clustering result is 95.65% and recognition time is 0.10. On the other hand in BP-ANN result is 93.47% and recognition time is 0.8.
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