A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint. (February 2017)

• Record Type: Journal Article
• Authors: Bhardwaj, Ishan; Londhe, Narendra D.; Kopparapu, Sunil K.
• Is Part Of: Pattern recognition. Volume 62(2017:Feb.)
• Page Start: 214
• Page End: 224
• Publication Date: 2017-02
• Subjects: Biometrics -- Fingerprint -- Dynamics -- Spoof attack -- Multimodal -- Behavioral -- Fusion -- LivDet -- ATVS; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.09.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Despite emerging as a prominent choice to serve the security concerns of person authentication applications, unimodal biometric systems are vulnerable to spoof attacks. Multimodal biometric systems can effectively minimize spoof attacks while improving the overall performance. In this paper, we present a multimodal system based on two modalities derived from multi instance fingerprint acquisition viz. fingerprint and the associated time dynamics. Extensive user verification and spoof resistance experiments conducted on virtual multimodal databases, created by combining ATVS and LivDet-13 fingerprint databases each with fingerprint dynamics database. Fusion is performed at match score level using sum and weighted sum rules. The empirical results demonstrate spoof resistance of the proposed multimodal system with significant performance improvement over unimodal and multi-instance fingerprint recognition systems. The performance of the proposed system is evaluated on well-known metrics like Detection Error Trade-off (DET) curves, equal error rate (EER), and Area Under the Curve (AUC). Graphical abstract: Highlights: Multimodal system based on physiological and behavioral characteristics is proposed. Experiments are conducted in scenarios: unimodal, multi-instance and multimodal. The system performance evaluation is performed in the presence of spoof samples. Average relative improvement in EER over unimodal is 90.64%. Average relative improvement in EER over multi-instance system is 82.57%.