An encoded histogram of ridge bifurcations and contours for fingerprint presentation attack detection

Abstract

In recent years, the exponential growth of internet technologies has made personal authentication an integral part of security applications. The fingerprint-based biometric systems are essentially used to safeguard the users' privacy and confidentiality. However, such systems are prone to spoof attacks by artificial replicas of the fingerprints. This paper presents an improved feature extractor called BiRi-PAD (Encoded Histogram of Ridge Bifurcations and Contours for fingerprint Presentation Attack Detection) that aims to enhance the accuracy of live fingerprint detection. The feature extraction process of the proposed BiRi-PAD consists of four steps. First, the fingerprint image undergoes a process of extracting 2-channel ridge contour maps (2-RC maps). The first channel of 2-RC maps consists of ridge contours extracted by a set of derivative filters in the spatial domain whereas the second channel of 2-RC maps consists of the ridge contours extracted by the maximum moments based on phase congruency in the frequency domain. Further, minutiae-based feature information i.e., ridge bifurcations are extracted by the minimum moments based on phase congruency covariance. Moreover, a fusion equation is proposed to integrate 2-RC maps and bifurcations into a single feature map. Second, an improved Comprehensive Local Phase Quantization (CLPQ) based on the well-known feature descriptor Rotation Invariant Local Phase Quantization (LPQri) is proposed to extract the phase information of ridges. CLPQ extracts the orientation of the ridges by using the complex parts of the significant frequency components of LPQri and monogenic filters. Third, the proposed BiRi-PAD quantizes the 2-RC maps into pre-determined intervals. Finally, both 2-RC maps and CLPQ features are integrated to generate a feature vector of a single fingerprint image. Performance evaluations of BiRi-PAD are conducted on three publicly available benchmarks from the LivDet competition, namely LivDet 2013, 2011, and 2015. Experimental evaluations demonstrate that the proposed BiRi-PAD achieves significant reductions in average rates compared to state-of-the-art techniques of fingerprint liveness detection. Specifically, on LivDet 2013, LivDet 2011, and LivDet 2015, the average rates are reduced to 1.92%, 4.39%, and 4.55%, respectively.