Abstract
This paper introduces a new approach, TIZMs & MNN, for palmprint recognition. It uses translation invariant Zernike moments (TIZMs) as palm features, and a modular neural network (MNN) as classifier. Translation invariance is added to the general Zernike moments which have very good property of rotation invariance. A fast algorithm for computing the TIZMs is adopted to improve the computation speed. The pattern set is set up by eight-order TIZMs. Because palmprint recognition is a large-scale multi-class task, it is quite difficult for a single multilayer perceptrons to be competent. A modular neural network is presented to act the classifier, which can decompose the palmprint recognition task into a series of smaller and simpler two-class sub-problems. Simulations have been done on the Polyu_PalmprintDB database. Experimental results demonstrate that higher identification rate and recognition rate are achieved by the proposed method in contrast with the straight-line segments (SLS) based method [2].
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, Y., Wang, K., Zhang, D. (2005). Palmprint Recognition Based on Translation Invariant Zernike Moments and Modular Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_28
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DOI: https://doi.org/10.1007/11427445_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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