Abstract
Recently, attempts at spoofing biometric fingerprint recognition systems through fake fingerprints have been frequently reported. Most existing fake fingerprint detection methods require either additional sensors or complicated calculations. In this paper, a fake fingerprint detection method is proposed that employs combinations of six simple statistical moment features. These features are deviation, variance, skewness, kurtosis, hyperskewness, and hyperflatness of the fingerprints. In addition, the average brightness, standard deviation, and differential features are considered. Of all features, the best ones are selected in terms of the overlap ratio between real and fake fingerprint images. The multi-dimensional features are combined at the feature level through a support vector machine. Based on the experimental results, the proposed method showed classification accuracy of approximately 99%.
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