Fingerprint reference point identification based on chain encoded discrete curvature and bending energy

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• 作者：Geevar C. Zacharias ; Madhu S. Nair ; P. Sojan Lal
• 关键词：Fingerprint ; Reference point ; Chain code ; k ; curvature ; Rotational invariance ; Bending energy
• 刊名：Pattern Analysis and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：20
• 期：1
• 页码：253-267
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition;
• 出版者：Springer London
• ISSN：1433-755X
• 卷排序：20

文摘

Reference point identification is important in automatic fingerprint recognition system as it can be used to align fingerprints in a correct orientation in spite of the possibility of different transformations in fingerprint images. It is also used in fingerprint classification, as it is desirable to classify fingerprint images for forensic type applications which require the input image to be verified against a large database. The important feature information useful for classification is centered near the reference point. Most of the current approaches for identifying the reference point either require determining ridge orientation or use some complex filters. These methods either operate on 2D (two dimensional) or are not robust to rotation or cannot be applied to every class of fingerprint image. This paper proposes a method to reliably identify unique reference point that operates in 1D (one dimensional). The method treats the fingerprint ridges as a non-overlapped sequence of chain code segments. A modified k-curvature method has been proposed to find the high-curvature area of fingerprint ridges. The reference point localization is based on the property of the ridge’s bending energy. The proposed method is tested on FVC2002 and FVC2004 standard datasets, and the experimental results show that the proposed algorithm can accurately locate reference point for all types of fingerprint images.