Eliminating Forgers Based on Intra Trial Variability in Online Signature Verification Using Handglove and Photometric Signals

Andrews Samraj, Shohel Sayeed, Loo Chu Kiong, Nikos E. Mastorokis

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DOI: 10.4236/jis.2010.11003                

Abstract

The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is the emerging technology. Skilled forgers try to attempt the genuine signatures in many numbers of trials. The wide gap in the Euclidian distances between forgers and the genuine template features prohibits them from successful forging. This has been proved by our repeated experiments on various subjects using the above combinational features. In addition the intra trial features captured during the forge attempts also differs widely in the case of forgers and are not consistent that of a genuine signature. This is caused by the pulse characteristics and degree of bilateral hand dimensional similarity, and the degrees of pulse delay. Since this economical and simple optical-based technology is offering an improved biometric security, it is essential to look for other reinforcements such the variability factor considerations which we proved of worth considering.

Keywords

Photo Plethysmography, Data Glove, Signature Verification, Multimodal Biometrics

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine In-telligence, Vol. 22, No. 1, 2000, pp. 63-84.
[2]	S. Rhee, B.-H. Yang and H. H. Asada, “Modelling of Finger Photoplethysmography for Wearable Sensors,” Proceedings of 21st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, At-lanta, 1999.
[3]	Y. Y. Gu, Y. Zhang and Y. T. Zhang, “A Novel Biometric Approach in Human Verification by Photophlytes-mograpic Signals,” Proceedings of the 4th IEEE confe-rence on Information Technology Applications in Biome-dicine, 2003, pp. 13-14.
[4]	A. Samraj, N. G. Noma and S. Sayed, “Quantification of Emotional Features on Phtoplethysomogrpic Wave Forms Using Box Counting Method of Fractal Dimention,” Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal, Processing (CSECS’09), Puerto De La Cruz, 2009, pp. 24-29.
[5]	J. C. Yao, X. D. Sun and Y. B. wan, “A Pilot Study on Using Derivatives of Photop Phlythesomogrpic Signals as Biometric Identifier,” Proceedings of 24th Annual Inter-national Conference of the IEEE EMBS, 2007, pp. 4576-4579.
[6]	B. Majhi, Y. Santhosh Reddy and D. Prassanna Babu, “Novel Features for off-Line Signature Verification,” In-ternational Journal of Computers Communication & Control, Vol. 1, No. 1, 2006, pp. 17-24.
[7]	“SVD and Signal Processing: Algorithms, Applications and Architectures,” F. Deprettere, Ed., North Holland Publishing Co., Amsterdam, 1989.
[8]	N. S. Kamel, S. sayeed and G. A. Ellis, “Glove Based Approach to Online Signature Verification,” IEEE Transactions on Pattern Analysis and Machine Intelli-gence, Vol. 30, No. 5, 2008, pp. 1-5.