Quality Assessment for Fingerprints Collected by Smartphone Cameras

Guoqiang Li, Bian Yang, Martin Aastrup Olsen, Christoph Busch; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 146-153

Abstract


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" or "non-high-quality" (including the low quality case and the background block case). A quality score is then generated for the whole sample. Experiments show this approach performs well in identifying the high quality blocks the Spearman correlation coefficient between the proposed quality scores and samples' normalized comparison scores (ground truth) reaches 0.53 while the rate of false detection (background blocks judged as high-quality ones) is still low as 4.63 percent over a challenging dataset collected under various real-life scenarios.

Related Material


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[bibtex]
@InProceedings{Li_2013_CVPR_Workshops,
author = {Li, Guoqiang and Yang, Bian and Olsen, Martin Aastrup and Busch, Christoph},
title = {Quality Assessment for Fingerprints Collected by Smartphone Cameras},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}