Fake fingerprint liveness detection based on micro and macro features

Fingerprint is the most hopeful biometric authentication that can specifically identify a person from their exclusive features. In the proposed approach, a novel software-based classification method is presented to classify between fake and real fingerprint. The intention of the proposed system is to improve the security of biometric identification system. The statistical techniques are good for micro features but not well for macro. In this paper, we present a novel combination of local Haralick micro texture features with macro features derived from neighbourhood gray-tone difference matrix (NGTDM) to generate an effective feature vector. Combined extracted features of training and testing images are passed to support vector machine for discriminating live and fake fingerprints. The proposed approach is experimented and validated on ATVS dataset and LivDet2011 dataset. The proposed approach has achieved good accuracy and less error rate in comparison with previously studied techniques.