Fingerprint and Iris liveness detection using invariant feature-set

Presentation attacks that make the biometric systems vulnerable has become a growing concern in recent years keeping in view its widespread applications in the field of banking, medical, security systems etc. For instance, textured contact lenses, high-quality printouts and fabricated synthetic materials spoof the iris texture and fingerprints that lead to increase in false rejection. Till now, extensive work has been done on global features. However, this paper proposed local features with invariance properties. Thus, the paper proposes detection of spoofing attacks in which local features are extracted for micro-textural analysis with properties of invariance to scale, rotation and translation. The features are encoded using Lehmer code and transformed into histograms that act as feature descriptors for classification. The top 4 features are selected using Friedman test. Experiments are simulated on iris spoofing databases: IIITD-Contact Lens, IIITD-Iris Spoofing, Clarkson-2015, Warsaw-2015and fingerprint spoofing databases: LivDet-2013 and LivDet-2015. Results have been validated through intra-sensor, inter-sensor, cross-sensor and cross-material. In case of IIITD-CLI, an EER of 1.36% and an ACER of 1.45% is obtained. For IIS, 0.94% of EER and 1.61% of ACER is observed. For Clarkson database, 0.79% of EER and 2.10% of ACER is obtained. An ACER of 0.57% is obtained for LivDet-2013 and 0.47% for LivDet-2015.