Abstract
Newborn and infant personal authentication is a critical issue for hospital, birthing centers, and other institutions where multiple births occur, which has not been well studied in the past. In this paper, we propose a novel online newborn personal authentication system for this issue based on footprint recognition. Compared with traditional offline footprinting scheme, the proposed system can capture digital footprint images with high quality. We also develop a preprocessing method for orientation and scale normalization. In this way, a coordinate system is defined to align the images, and a region of interest (ROI) is cropped. In recognition stage, four orientation feature-based approaches, Ordinal Code, BOCV, Competitive Code, and Robust Line Orientation Code, are exploited for recognition. A newborn footprint database is established to examine the performance of the proposed system, and promising experimental results demonstrate the effectiveness of the proposed system.
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Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 60705007 & 61005010 & 60805021 & 60975005, the grant of the Knowledge Innovation Program of the Chinese Academy of Sciences Y023A11292 & Y023A61121, the grant of China Postdoctoral Science Foundation, No. 20100480708, the grant of the Key Scientific Research Foundation of Education Department of Anhui Province, No. KJ2010A289, and the grant of Scientific Research Foundation for Talents of Hefei University, No. 11RC05.
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Jia, W., Cai, HY., Gui, J. et al. Newborn footprint recognition using orientation feature. Neural Comput & Applic 21, 1855–1863 (2012). https://doi.org/10.1007/s00521-011-0530-9
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DOI: https://doi.org/10.1007/s00521-011-0530-9