Abstract
Automatic personal identification based on finger-knuckle-print (FKP) has been considered as a promising technology in biometrics family in recent years. Previous work indicates that local orientation analysis supplies an efficient framework for FKP representation. In this paper, we propose a novel FKP recognition method using the Adaptive Steerable Orientation Coding (ASOC). High order steerable filters are first employed to extract the continuous orientation feature map, then we use multilevel histogram thresholding method to quantize the feature map adaptively and the discrete orientations are used for coding a FKP image. Furthermore, we measure the similarity between two coded FKP images by designing an effective angular matching function. Experimental results on the PolyU FKP database demonstrate the accuracy of the proposed method.
The work is partially supported by the NSFC funds of China under Contract No.s 61173086, 60902099, and 61179009, and Harbin special funds for innovative talents of science and technology research project.
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Li, Z., Wang, K., Zuo, W. (2012). Finger-Knuckle-Print Recognition Using Local Orientation Feature Based on Steerable Filter. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_33
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DOI: https://doi.org/10.1007/978-3-642-31837-5_33
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