Abstract
This work introduces a new approach to detect fake fingers, based on the analysis of time-series fingerprint images. When a user puts a finger on the scanner surface, a time-series sequence of fingerprint images is captured. Five features are extracted from the image sequence. Two features represent the skin elasticity, and three features represent the physiological process of perspiration. Finally the Support Vector Matching (SVM) is used to discriminate the finger skin from other materials such as gelatin. The experiments carried out on a dataset of real and fake fingers show that the proposed approach and features are effective in fake finger detection.
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Jia, J., Cai, L. (2007). Fake Finger Detection Based on Time-Series Fingerprint Image Analysis. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_116
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DOI: https://doi.org/10.1007/978-3-540-74171-8_116
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