Comparison of Classification Methods for Time-Series Detection of Perspiration as a Liveness Test in Fingerprint Devices

Abstract

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.