Qualifying fingerprint samples captured by smartphone cameras

This paper proposes an approach to qualifying fingerprint samples captured by smartphone cameras under real-life scenarios, foreseeing the future application using such general purposed cameras as fingerprint sensors. In this approach, a sample image is first divided into non-overlapping blocks. Then a 7-dimensional feature vector will be formed from the proposed 7 quality features. We use a support vector machine to produce a binary indication for each image block on its quality. Finally a quality score is generated to indicate the whole fingerprint sample's quality by counting the number of qualified blocks in a sample. Experiments demonstrate the proposed approach's capability of qualifying such quality-challenging fingerprint samples - the Spearman's rank correlation coefficient ρ between the proposed quality metric and samples' normalized comparison scores reaches as high as 0.53 in our experiment.