Fingerprint Image Classification using Singular Points and Orientation Information
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 9
Abstract
The fingerprint classification process is an essential task that reduces fingerprint matching time of an Automatic fingerprint identification system, where a large database is used. It is still a challenging task. The proposed work classifies the fingerprints by using singular points and orientation information below the core point. As the first step of proposed work, features like orientation field and singular points are extracted. Orientation field is estimated using multi-scale principal component analysis and singular points are detected using shape analysis of binary candidate region image. To speed up the classification process, rules based on location of singular points and orientation information below upper core point are used for classification. The proposed work is tested on most popular public NIST special database 4 and experimental results show that classification accuracy is 92.2 % for five-class problem and 97.35% for four-class problem without rejection. Also, the proposed work classifies more accurately the ambiguous fingerprint images into its primary as well as secondary class.
Authors and Affiliations
K. S. Jeyalakshmi, T. Kathirvalava kumar
Keywords
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- EP ID EP392134
- DOI 10.9790/9622-0709023342.
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