Gabor Filter-Based Fingerprint Anti-spoofing

摘要

This paper describes Gabor filter-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Tex-tural measures based on Gabor energy and co-occurrence texture features are used to characterize fingerprint texture. Fingerprint image is filtered using a bank of four Gabor filters, and then a gray level co-occurrence matrix (GLCM) method is applied to filtered images to extract minute textural details. Dimensionality of the features is reduced by principal component analysis (PCA). We test features on three different classifiers: neural network, support vector machine and OneR; then we fuse all the classifiers using the "Max Rule" to form a hybrid classifier. Overall classification rates achieved with various classifiers range from ～94.12% to ～97.65%. Thus, the experimental results indicate that, the new liveness detection approach is a very promising technique, as it needs only one fingerprint and no extra hardware to detect vitality.