Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies, using a larger, more diverse data set, and a shorter time window. Several classification methods were tested on fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and 14 cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Schuckers, S. A. C., Parthasaradhi, S. T. V., Derakshani, R., & Hornak, L. A. (2004). Comparison of classification methods for time-series detection of perspiration as a liveness test in fingerprint devices. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-25948-0_36
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