Abstract
3D palmprint and 3D ear based systems, two representative 3D biometric systems, have recently led to a proliferation of studies. Previous works mainly concentrated on solving one-to-one verification problems, but they cannot deal with the one-to-many identification problems quite well. Quite recently, collaborative representation (CR) framework has been exploited to solve such identification problems. The original CR based method used the whole range data for classification. However, apart from the discriminative information, the whole range data also inevitably contains confusing noises, which adversely affect the classification result. To solve this problem, we propose a multi-dictionary based collaborative representation method to separate the discriminative information from the noises. Specifically, we divide the testing image into several blocks, compute reconstruction residuals for each block using CR framework, and finally fuse the residuals to predict the class label. Experiments on benchmark datasets demonstrate that our method greatly outperforms previous one-to-many identification methods both in classification accuracy and computational complexity.
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Yang, A., Zhang, L., Li, L., Li, H. (2016). Multi-dictionary Based Collaborative Representation for 3D Biometrics. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_5
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DOI: https://doi.org/10.1007/978-3-319-42291-6_5
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