The rapid momentum in the realization of security solutions, availability of affordable hardware components, and computing devices has led to a tremendous rise in biometric research. However, the threat of spoofing has raised palpable security concerns. In this paper, we examined a recently introduced novel behavioral biometrics technique, namely, fingerprint dynamics. The technique exploits individual’s behavioral characteristics observed from multi-instance finger scan events. The objective of this investigation was to study the spoof resistance capabilities of the fingerprint dynamics-based standalone identity verification system. We used a custom-built hardware unit to collect biometric samples from a total of 50 participants, in an environment that closely mimics the operational scenario. Data collection was done in several sessions per user, spread over a period of seven weeks. We performed an exhaustive analysis of several time-derived features, and selected a combination of best-performing features using genetic algorithm. We also conducted a systematic evaluation using support vector machine and    $k$   -nearest neighbor classifiers. We performed a series of verification experiments under three different and practically relevant attack scenarios, namely: 1) combined zero-effort and active imposter; 2) only zero-effort imposter; and 3) only active imposter. We find that the proposed technique exhibits promising results under all the three attack scenarios.
['Ishan Bhardwaj', 'Narendra D. Londhe', 'Sunil Kumar Kopparapu']
Study of Imposter Attacks on Novel Fingerprint Dynamics Based Verification System