Abstract
Fingerprint recognition systems are widely used for authentication purposes in security systems. However, fingerprint recognition systems can easily be spoofed by imitations of fingerprints using various spoof materials. A compact and discriminative set of features is needed to discriminate between live and spoof fingerprints. We explore combined Shepard magnitude and orientation for live fingerprint detection using independent quantization of global and local features extracted in spatial and frequency domain. The spatial domain features that are extracted comprise of the magnitude of perceived spatial stimuli that is computed from the net variation of perceived edge information. Rotation invariance is achieved by extracting local features based on phase information of significant frequency components in the frequency domain. The concatenated feature vector associated with a fingerprint image is represented as a two-dimensional histogram. The support vector machine classifier is used to classify the fingerprint as either live or spoof. Experiments are performed on three databases, i.e., the fingerprint liveness detection (LivDet) competition databases of 2011, 2013, and 2015. Results showed a reduction in average error rate to 5.8, 2.2, and 5.3 on LivDet 2011, 2013, and 2015, respectively.