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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...
It is becoming feasible and promising to use general purposed smartphone cameras as fingerprint scanners due to the rapidly improvement of smartphone hardware performance. We propose an approach to qualify the fingerprint samples generated by smartphones' cameras under real-life scenarios. Firstly, our approach extracts 6 quality features for each image block divided from a fingerprint sample using...
We propose an approach to assess the quality of fingerprint samples captured by smartphone cameras under real-life scenarios. Our approach extracts a set of quality features for image blocks. Without needing segmentation, the approach determines a sample's quality by checking all image blocks divided from the sample and for each block a trained support vector machine gives a binary indication - "high-quality"...
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