Authentication based on feature of hand-written signature

Abstract

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures. In the hand-written signature process 10 biometric features were summarized: the amount of zero speed in direction x and direction y, the amount of zero acceleration in direction x and direction y, the total time of the hand-written signatures, the total distance of the pen traveling in the hand-written process, the frequency for lifting the pen, the time for lifting the pen, the amount of the pressure higher or lower than the threshold values. The formulae of biometric features extraction were summarized. The Gauss function was used to draw the typical information from the above-mentioned biometric features, with which to establish the hidden Markov mode and to train it. The frame of double authentication was proposed by combing the signature with the digital signature. Web service technology was applied in the system to ensure the security of data transmission. The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Keywords

behavioral biostatistics feature / hand-written signature / hidden Markov mode / signature verification

References

[1] JainA. K., HongL., PankantiS.. Biometrics identification[J]. Communications of the ACM, 2000, 43(2): 91-98

[2] SchneierB.. Inside risks: The uses and abuses of biometrics[J]. Communications of the ACM, 1999, 42(8): 136-143

[3] RathaN. K., ConnellJ. H., BokR. M.. Enhancing security and privacy in biometrics-based authentication systems[J]. IBM Systems Journal, 2001, 40(3): 614-634

[4] ZhuS.-ren.The Theory and Application of Behavior Biostatistics Features Authentication[M], 2002, Changsha, National University of Defence Technology

[5] ZhangD.-xia.. The on-line hand-written characters identify system based on ANN and HMM[J]. Control & Automation, 2005, 21(8): 44-46

[6] LuH.-ru.. The summarize of hand-written characters identify[J]. Computer Application and Software, 1994, 11(2): 1-8

[7] SabourinR., Jean-PierreD.. Off-line signature verification using directional PDF and neural networks[J]. Pattern Recognition, 2000, 29(6): 415-424

[8] MitalD. P., ChooP. H., WeeK. L.. Computerized signature verification system[J]. IEEE Control Systems Magazine, 1988, 24(5): 54-57

[9] ColmenarezA., FreyB., HuangT. S.. A probabilistic framework for embedded face and facial expression recognition[J]. IEEE Computer Vision and Pattern Recognition, 1999, 20(3): 592-597

[10] ColomboC., BimboA. D.. Real time head tracking from the deformation of eye contours using a piecewise affine camera[J]. Pattern Recognition Letters, 1999, 20(7): 721-730

[11] PlamondonR., LoretteG.. Automatic signature verification and writer identification: The state of the art[J]. Pattern Recognition, 2002, 22(4): 107-131

[12] PlamondonR., SargurN. S.. On-line and off-line handwriting recognition: A comprehensive survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 22(3): 63-84

[13] KiatH. K., HeikoS., LeedhamG.. Codebooks for signature verification and handwriting recognition[J]. Information Systems, 2002, 22(2): 44-48

[14] ZhuS.-r., DengT.-ting.. Fuzzy matching routing filter in content-based publish-subscribe[J]. Journal of Central South University: Science and Technology, 2007, 38(1): 138-142

[15] ZhuS.-r., LiW.-qin.. Design and implementation of self-protection agent for network-based intrusion detection system[J]. Journal of Central South University of Technology, 2003, 10(1): 69-73

[16] PudilP., NovovicovaJ., KittlerJ.. Floating search methods in feature selection[J]. Pattern Recognition Letters, 1994, 15(11): 1119-1125

[17] JinA. T. B., LingD. N. C., GohA.. Boihashing: Two factor authentication featuring fingerprint data and tokenised random number[J]. Pattern Recognition, 2004, 37(11): 2245-2255

[18] MizukamiY., YoshimuraM., MiikeH.. An off-line signature verification system using an extracted displacement function[J]. Pattern Recognition, 2003, 36(1): 91-101

[19] JustinoE. J. R., BortolozziF., SabourinR.. A comparison of SVM and HMM classifiers in the off-line signature verification[J]. Pattern Recognition Letters, 2005, 26(9): 1377-1385

[20] BaronR., PlamondonR.. Acceleration measurement with an instrumented pen for signature verification and handwriting analysis[J]. IEEE Transactions on Instrumentation and Measurement, 1989, 38(6): 1132-1138

[21] HeL., GovindarajuV.. A comparative study on the consistency of features in on-line signature verification[J]. Pattern Recognition Letters, 2005, 26(15): 2483-2489