Examination of Complex Signatures for Conclusive Opinion

Vishvendra Narayan¹ , Munish Kumar Mishra² , Lav Kesharwani³ , Neha Yadav⁴

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.1 , pp.42-50, Jan-2019

Online published on Jan 31, 2019

Copyright © Vishvendra Narayan, Munish Kumar Mishra, Lav Kesharwani, Neha Yadav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The verification of signatures handwritten signatures is one of the oldest and most popular biometric authentication methods in our society. A history which spends several hundred years have ensured that is has a wide legal acceptance all around the world. A methodology adopted for examination of complex signature based on angle measurement of cross-section of strokes in signature that was previously validated has been applied to 30 writers in order to quantify the degree of angle of cross-section of stroke at the 5 different points at different cross-section among handwritten signatures. In this methodology, the discriminating power of parameter extracted from signature was investigated. The angle of connecting strokes (1st to 5th point) appeared to be most discriminant. Finally, the difference between writers could be characterized more precisely. The individual writers were distinguished by the variability of parameters of angle of their cross-section in stroke and the morphological cross-section between its different letters. The correct discrimination rates reached in the study suggested that carrying out an expertise of fragmentary samples of handwriting comprising only angle of cross-section is completely possible.

Key-Words / Index Term :
Forensic science Signature, forged signature, complex signature

References :
[1]. Cha (2001), Comprehensive survey on distance/similarity measures between probability density functions, international journal of mathematical models and methods in applied sciences. Issue 4, volume 1
[2]. Cha, S. H., and Srihari, S.N. (2000), writer identification: Statistical Analysis and Dichomizer, Proc. SPR & SSPR Alicante, LNCS-Advance in Pattern Recognition, vol. 1876, pp 123-132.
[3]. E. Chartrel, and A. Vinter (2008). The impact of spatio-temporal constraints on cursive letter handwriting in children. In journal of Forensic Science Learning and instruction 18 537e47.
[4]. Cheng, Nai-jen, Wen Chi-Jan and Hon-Fai Yau, (1998) Handwritten Chinese signature verification based on attributed string matching of stroke linkage order, 32nd Annual 1998 International Carnahan Conference pp-238-243.
[5]. Ericsson, K; Forssell, L.G.; Holmén, K; Viitanen, M. and Winblad B. (1996), “Copying and handwriting ability in the screening of cognitive dysfunction in old age, PubMed - indexed for MEDLINE, volume 22, page no.103-121.
[6]. Fang, B., Leung, C. and Tang, Y, (2003) Off-line signature verification by the tracking of feature and stroke positions, vol. 36, pp-91-101.
[7]. Fisher, and Pearson, Karl (1970), “analysis of variation” studies in the history of statistics and probability 55,445-457
[8]. Furuhashi, T., Yang, X., Uchikawa, Y. and Obata, K. (1995) learning Systems, Man and Cybernetics, ‘Intelligent Systems for the 21st Century’. IEEE International Conference vol.(5), pp- 4383-4386.
[9]. Han Ke, K., Ishwar, Sethi. (1996) Handwritten signature retrieval and identification, Vision and Neural Networks Laboratory, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA vol.(17), 83-90.
[10]. Huber and Headrik, (1999) “Handwriting identification facts and fundamentals, CRC Press, Boca Raton, pp 100 and 133-134.
[11]. Kapoor, T. S. and Sharma R. (1985), “Study of the form & extent of natural variation in genuine writing with age”, Journal of Forensic Science society, volume 25, page no. 371-375.
[12]. Kapoor,T. S., Kapoor, M., Sharma, G.M. (1985), “age effect on handwriting” Journal of forensic society Volume 25 issue-5, September. pp 371-375.
[13]. Lee S. and Pan J. C, (1992), “Off line tracing and representation of signature”, vol. 22, pp- 755-771.
[14]. Levinson, J. (2001), Questioned Documents a lawyer’s Handbook. Academic press, London, pp 1-57.
[15]. M. G. Longstaff, and R. A. Heath (2003), “the influence of motor system degradation on the control of handwriting movements” A dynamical systems analysis, Science/Human movement Science, 22 91-110.
[16]. R. Marquis, F. Taroni S, Bozza, M, Schmittbuhl. (2006), “Quantitative characterization of morphological polymorphism of handwritten characters loops” Forensic Science International 164 pp 211–220.
[17]. S. Srihari, S. Cha, H. Arora, and S. Lee, (2002), “Individuality of handwriting,” in Journal of Forensic Sciences, vol. 47, no.4. ASTM International, pp. 1–17.
[18]. Bulacu, M. Schomarker, L. (2000), “identification of person by proof of handwriting” 10 SW (2D) 1106.
[19]. Solar Javier Ruiz-Del, Devia Christ, Loncomilla Patricio and Concha Felipe (2008), Offline Signature Verification Using Local Interest Points and Descriptors, Progress in Pattern Recognition, Image Analysis and Applications, pp-22-29.
[20]. Steinherz, Doermann and Rivlin, (2009), Off-line loop investigation for handwriting analysis, vol.(31),pp- 193-209.
[21]. Thomassen, A., H. Teulings, (1985), Time, size, and shape in handwriting: exploring spatiotemporal relationships at different levels In: Michon, J., and Jackson, J. (eds.), Time, Mind, and Behavior, Springer-Verlag.
[22]. Wang Y. Y, Leung C. H. and Tang Y. Y, (2001), Offline signature verification by the analysis of cursive strokes, vol. (15), pp. 659-673.
[23]. Zhang, B. and Srihari, S. N. (2003), Analysis of handwriting individuality using word features, in: Proceedings of the Seventh international Conference on Document Analysis and Recognition (ICDAR), Edinburgh, Scotland, pp 1142-1146.