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http://dx.doi.org/10.5391/JKIIS.2010.20.5.701

An Efficient Signature Recognition Based on Histogram Using Statistical Characteristics  

Cho, Yong-Hyun (School of Computer and Information Comm. Eng., Catholic Univ. of Daegu)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.5, 2010 , pp. 701-709 More about this Journal
Abstract
This paper presents an efficient signature recognition method by using the hybrid similarity criterion, which is in inverse proportion to distance and in proportion to correlation between the images. The distance is applied to express the spacial property of image, and the correlation is also applied to express the statistical property. The proposed criterion provides the robust recognition to both the geometrical variations such as position, size, and rotation and the shape variation. The normalized cross-correlation(NCC), which is calculated by considering 4 directions based on the histogram of binary image, is applied to express rapidly and accurately the similarity between the images. The proposed method has been applied to the problem for recognizing the 20 truck images of 288*288 pixels and the 105(3 persons * 35 images) signature images of 256*256 pixels, respectively. The experimental results show that the proposed method has a superior recognition performance that appears the image characters well. Especially, the hybrid criterion of NCC and ordinal distance has a superior recognition performance to the hybrid criterion using city-block or Euclidean distance.
Keywords
Signature recognition; Similarity criterion; Distance; Normalized cross-correlation; Histogram of binary image;
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1 F. D. Jou, K. C. Fan, and Y. L. Chang, "Efficient Matching of Large-size Histograms," Pattern Recognition Letters, Vol.25, pp.277-286, Feb. 2004.   DOI
2 S. H. Cha, "Taxonomy of Nominal Type Histogram Distance Measures," American Conference on Applied Mathematics, Harvard, Massachusetts, USA, pp.325-330, Mar. 2008.
3 T. Kailath, "The Divergence and Bhattacharyya Distance Measures in Signal Selection," IEEE Trans. on Comm. Technology, Vol.15, pp.52-60, Feb. 1967.   DOI
4 S. H. Cha and S. N. Srihari, "On Measuring the Distance between Histogram," Pattern Recognition, Vol.35, pp.1355-1370, June 2002.   DOI
5 X. Yu and M. K. H. Leung, "Shape Recognition using Curve Segment Hausdorff Distance," 18th International Conference on Pattern Recognition. Vol.3, pp.441-444, Aug. 2006.
6 R. M. Aarts, R. Irwan, and A. J. E. M. Janssen, "Efficient Tracking of the Cross-correlation Coefficient," IEEE. Trans. on Speech Audio Process, Vol. 10, No.6, pp.391-402, Sep. 2002.   DOI
7 F. Zhao, Q. Huang, and W. Gao, "Image Matching by Normalized Cross-Correlation," ICASSP 2006, Vol.2, pp.729-732, May 2006.
8 T. Ivry, S. Michal, A. Avihoo, G. Sapiro, and D. Barash, "An Image Processing Approach to Computing Distances between RNA Secondary Structures Dot Plots," Algorithms Mol. Biol.. Vol.4, pp.1-19, Feb. 2009.   DOI
9 김진형, “온라인 서명 검증의 현황 및 방법론 소개,” http:://ai.kaist.ac.kr/~jkim, 2001년 2월
10 A. A Kholmatov, “Biometric Identity Verification Using On-line & Off-line Signature Verification”, Master of Science Thesis, Sabanci University, Spring 2003.
11 H. Baltzakis and N. Papamorkos, "A New Signature Verification Technique Based on a Two-stage Neural Network Classifier.", Engineering Application of Intelligence, Vol.14, pp.95-103, Feb. 2001.   DOI
12 O. Cemil, F. Ercal, and Z. Demir, "Signature Recognition and Verification with ANN” ELECO'03, Dec. 2003.
13 F. Serratosa and A. Sanfeliu, "Signatures versus Histograms : Definitions, Distances and Algorithms," Pattern Recognition, Vol.39, pp.921-934, May 2006.   DOI