Fingerprint Anti-Spoofing Using Ridgelet Transform

This paper proposes a new ridgelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses differences in textural characteristics observed in real and spoof fingerprints for spoof detection. Textural measures based on ridgelet energy signatures and ridgelet co-occurrence signatures are used to characterize fingerprint texture. Principal component analysis with ranker search is used to reduce dimensionalities of the feature sets. We test two feature sets independently on three classifiers: neural network, support vector machine and k-nearest neighbor; then we fuse all the classifiers using the "mean rule" to form an ensemble classifier. Classification rates achieved with these classifiers, including an ensemble classifier range from _~92.47% to _~97.41%. Thus, the performance of the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.