A minutiae-based fingerprint individuality model

Fingerprint individuality study deals with the crucial problem of the discriminative power of fingerprints for recognizing people. In this paper, we present a novel fingerprint individuality model based on minutiae, the most commonly used fingerprint feature. The probability of the false correspondence among fingerprints from different fingers is calculated by combining the distinctiveness of the spatial locations and directions of the minutiae. To validate our model, experiments were performed using different fingerprint databases. The matching score distribution predicted by our model actually fits the observed experimental results satisfactorily. Comparing to most previous fingerprint individuality models, our model makes more reasonably conservative estimate of the fingerprint discriminative power, making it a powerful tool for studying the fingerprint individuality as well as the performance evaluation of fingerprint verification systems.