Abstract
A fingerprint recognition procedure usually contains two stages: registration and authentication. Most fingerprint recognition systems capture multiple samples of the same finger (e.g., eight impressions of a person’s left index finger) at the stage of registration. As a result, it is essential to select several samples as templates. This paper proposes two algorithms maximum match scores (MMS) and greedy maximum match scores (GMMS) based on match scores for template selection. The proposed algorithms need not involve the specific details about the biometric data. Therefore, they are more flexible and can be used in various biometric systems. The two algorithms are compared with Random and sMDIST on the database of FVC2006DB1A, and the experimental results show that the proposed approaches can improve the accuracy of biometric system efficiently. Based on the maximized score model, we propose two strategies: ONLINE and OFFLINE for templates update and analyze the relationship between the two strategies. Preliminary experiments demonstrate that OFFLINE strategy gains better performance and GMMS performs better and can gain steady improvement.
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Li, Y., Yin, J., Zhu, E., Hu, C., Chen, H. (2009). Studies of Fingerprint Template Selection and Update. In: Kim, Th., et al. Advances in Computational Science and Engineering. FGCN 2008. Communications in Computer and Information Science, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10238-7_13
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DOI: https://doi.org/10.1007/978-3-642-10238-7_13
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