Abstract:

Fingerprint video was utilized for fingerprint verification. Inside similarity (S^I) and outside similarity (S^O) were defined and used to calculate the final matching score of two fingerprint videos. A new idea was proposed to acquire the optimized performance of fingerprint verification. A matching result with two features S^I and S^O was viewed as a sample. The task of verifying whether two fingerprint videos are genuine matching or impostor matching, was converted to classification task of samples with two-dimensional features (S^I,S^O). In additron, the machine learning algorithms were adopted to classify every matching result. Experimental results showed that the minimum error rates calculated through the method of machine learning algorithms were 0.1704％ and 0.1106％ while those calculated through the method of using threshold were 0.2229％ and 0.1700％. The accuracy of video-based fingerprint verification was significantly improved by using machine learning algorithm compared to the results by using threshold. And the current method avoided the complex process of selecting parameters and thresholds.

Key words:  fingerprint verification, video, machine learning