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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Fake Fingerprint Detection Based on Statistical Moments
verfasst von : Yosep Park, Unsoo Jang, Jiwon Im, Woohyuk Jang, Daejune Ko, Eui Chul Lee
Erschienen in: Advanced Multimedia and Ubiquitous Engineering
Verlag: Springer Singapore
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Abstract
Fingerprint recognition is a biometric method. Recently, attempts for spoofing of fingerprint recognition systems through fake fingerprints have been frequently reported. Most existing fake fingerprint detection methods require either additional sensors or complicated calculations. In the present study, a new fake fingerprint detection method implemented through the combinations of six simple statistical moment features is proposed. The six statistical moments mean the deviation, variance, skewness, kurtosis, hyperskewness, and hyperflatness. Average brightness, standard deviation, and differential feature are additionally used. The multi-dimensional features were combined through the Support Vector Machine. According to the results of experiments, the proposed method showed classification accuracy of about 98%.