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Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance

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Abstract

The paper presents an efficient on-line signature verification method based on the dynamic features of a given signature. In the proposed approach, curvature and torsion feature are associated with Hausdorff distance measure which can be used in the verification process. In the feature extraction step, the signature trajectory is approximated as a spatial curve. A set of curvature and torsion value of extreme point is computed from both x coordinate, y coordinate and pressure feature so that the dimension of the curve is reduced. Therefore, a new composed signature feature is created for each person. For the obtained feature data, the most distinctive Hausdorff distance is further proposed to calculate the distances of the eight-dimensional feature vector between the test signature and corresponding template signatures for the verification of the test sample. Comprehensive experiments are implemented on three publicly available databases: the SVC2004, SUSIG and MCYT-100 database. A comparison of our results with some recent signature verification methods available in the literature is provided with equal error rate, and the results indicate that the proposed method would better recognize genuine signatures, random and skilled forgeries.

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References

  1. Alaei A, Pal S, Pal U et al (2017) An Efficient Signature Verification Method Based on an Interval Symbolic Representation and a Fuzzy Similarity Measure. IEEE Transactions on Information Forensics & Security 12(10):2360–2372

    Article  Google Scholar 

  2. Alpar O, Krejcar O (2016) Hidden frequency feature in electronic signatures. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer International Publishing, Morioka, pp. 145–156

  3. Ansari AQ, Hanmandlu M, Kour J et al (2014) Online signature verification using segment-level fuzzy modelling. IET Biometrics 3(3):113–127

    Article  Google Scholar 

  4. Barkoula K, Economou G, Fotopoulos S (2013) Online signature verification based on signatures turning angle representation using longest common subsequence matching. International Journal on Document Analysis and Recognition (IJDAR) 16(3):261–272

    Article  Google Scholar 

  5. Che C, Yu X, Sun X, et al (2017) Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance. EURASIP Journal on Advances in Signal Processing, pp. 1–13

  6. Cpałka K, Zalasiński M (2014) On-line signature verification using vertical signature partitioning. Expert Syst Appl 41(9):4170–4180

    Article  Google Scholar 

  7. Diaz M, Fischer A, Ferrer MA et al (2018) Dynamic Signature Verification System Based on One Real Signature. IEEE Transactions on Cybernetics 48(1):228–239

    Article  Google Scholar 

  8. Doroz R, Porwik P (2011) Handwritten signature recognition with adaptive selection of behavioral features. In: 10th International Conference on Computer Information Systems and Industrial Management Applications (CISIM), Kolkata, pp. 128–136

  9. Doroz R, Porwik P, Orczyk T (2015) Dynamic signature verification method based on association of features with similarity measures. Neurocomputing 171(C):921–931

    Google Scholar 

  10. Fang L, Lu W, Huang W (2012) Estimate algorithms and embedded crafts of curvature and torsion. Journal of Graphics 33(2):9–13

    Google Scholar 

  11. Fang X, Wu S, Liu J (2017) Discrete curvature and torsion-based parameterization scheme for data points. In: 7-th International Conference on Computer Engineering & Networks, pp. 1–12

  12. Ghosh R, Roy PP (2017) Study of zone-based feature for online handwritten signature recognition and verification in Devanagari script. Proceedings of International Conference on Computer Vision and Image Processing. Springer Singapore, pp. 523–530

  13. Guerbai Y, Chibani Y, Hadjadji B (2015) The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recogn 48(1):103–113

    Article  Google Scholar 

  14. Guru DS, Manjunatha KS, Manjunath S, Somashekara MT (2017) Interval valued symbolic representation of writer dependent features for online signature verification. Expert Syst Appl 80:232–243

    Article  Google Scholar 

  15. Hafemann LG, Sabourin R, Oliveira LS (2015) Offline handwritten signature verification-literaturereview. Statistics 2015:1–8

  16. Hafemann LG, Sabourin R, Oliveira LS (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn 70:163–176

    Article  Google Scholar 

  17. Impedovo D, Pirlo G (2008) Automatic Signature Verification: The State of the Art. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 38(5):609–635

    Article  Google Scholar 

  18. Impedovo D, Pirlo G, Russo M (2014) Recent advances in offline signature identification. In: Proc. of the 14th International Conference on Frontiers in Handwriting Recognition, pp. 639–642

  19. Kar B, Mukherjee A, Dutta PK (2017) Stroke Point Warping-Based Reference Selection and Verification of Online Signature. IEEE Trans Instrum Meas 67(1):2–11

    Article  Google Scholar 

  20. Khoh WH, Ong TS, Pang YH et al (2014) Score level fusion approach in dynamic signature verification based on hybrid wavelet Fourier transform. Security & Communication Networks 7(7):1067–1078

    Article  Google Scholar 

  21. Kholmatov A, Yanikoglu B (2009) SUSIG: An On-line handwritten signature database, associated protocols and benchmark results. Pattern Anal Applic 12(3):227–236

  22. Leclerc F, Plamondon R (1994) Automatic signature verification: the state of the art-1989-1993. Int J Pattern Recognit Artif Intell 8(3):643–660

    Article  Google Scholar 

  23. Liu Y, Nie L, Han L et al (2016) Action2Activity: Recognizing Complex Activities from Sensor Data. Computer Vision and Pattern Recognition 2016:1–7

    Google Scholar 

  24. Liu Y, Nie L, Liu L et al (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  25. Liu Y, Yang Z, Yang L (2015) Online Signature Verification Based on DCT and Sparse Representation. IEEE Transactions on Cybernetics 45(11):2498–2511

    Article  Google Scholar 

  26. Liu S, Zhu YJ, Zhang LY (2005) Research on the algorithm for matching 2D contours. Jiangsu Machine Building & Automation 34(2):60–63

    Google Scholar 

  27. Liwicki M, Found B (2011) Signature verification competition for online and offline skilled forgeries (SigComp2011). In: International Conference on Document Analysis & Recognition. IEEE, pp. 1480–1484

  28. Liwicki M, Malik MI, Alewijnse L et al (2012) ICFHR 2012 competition on automatic forensic signature verification (4NsigComp 2012). In: International Conference on Frontiers in Handwriting Recognition. IEEE Computer Society

  29. Malik MI, Ahmed S, Marcelli A et al (2015) ICDAR2015 competition on signature verification and writer identification for on-and off-line skilled forgeries (SigWIcomp2015). In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE Computer Society, pp. 1186–1190

  30. Malik MI, Liwicki M, Alewijnse L et al (2013) ICDAR 2013 competitions on signature verification and writer identification for on-and off-line skilled forgeries (SigWiComp 2013). In: International Conference on Document Analysis & Recognition. IEEE Computer Society

  31. Mandal S, Prasanna SRM, Sundaram S (2018) GMM Posterior Features for Improving Online Handwriting Recognition. Expert Syst Appl 97:421–433

    Article  Google Scholar 

  32. Manjunatha KS, Manjunath S, Guru DS et al (2016) Online Signature Verification based on Writer Dependent Features and Classifiers. Pattern Recogn Lett 80(C):129–136

    Article  Google Scholar 

  33. Ooi SY, Teoh ABJ, Pang YH et al (2016) Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network. Appl Soft Comput 40(C):274–282

    Article  Google Scholar 

  34. Ortega-Garcia J, Fierrez-Aguilar J, Simon D et al (2003) MCYT baseline corpus: a bimodal biometric database. Vision, Image and Signal Processing, IEE Proceedings 150(6):395–401

    Article  Google Scholar 

  35. Palys M, Doroz R, Porwik P (2013) On-line signature recognition based on an analysis of dynamic feature. In: IEEE International Conference on Biometrics and Kansei Engineering (ICBAKE), Tokyo, pp. 103–107

  36. Patel OP, Tiwari A, Chaudhary R, et al (2017) Enhanced quantum-based neural network learning and its application to signature verification. Soft Computing, pp. 1–14

  37. Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84

    Article  Google Scholar 

  38. Porwik P, Doroz R (2014) Self-adaptive biometric classifier working on the reduced dataset. In: International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Artificial Intelligence, Springer Series 8480:377–388

  39. Porwik P, Doroz R, Orczyk T (2016) Signatures verification based on PNN classifier optimized by PSO algorithm. Pattern Recogn 60:998–1014

    Article  Google Scholar 

  40. Rohilla S, Sharma A (2016) SVM Based Online Signature Verification Technique Using Reference Feature Vector. Proceedings of the National Academy of Sciences India 87(1):1–12

    Google Scholar 

  41. Sae-Bae N, Memon N (2014) Online Signature Verification on Mobile Devices. IEEE Transactions on Information Forensics & Security 9(6):933–947

    Article  Google Scholar 

  42. Sharma A, Sundaram S (2016) An enhanced contextual DTW based system for online signature verification using Vector Quantization. Pattern Recogn Lett 84:22–28

    Article  Google Scholar 

  43. Sharma A, Sundaram S (2017) A novel online signature verification system based on GMM features in a DTW framework. IEEE Transactions on Information Forensics and Security 12(3):705–718

    Article  Google Scholar 

  44. Sharma A, Sundaram S (2018) On the Exploration of Information From the DTW Cost Matrix for Online Signature Verification. IEEE Transactions on Cybernetics 48(2):611–624

    Article  Google Scholar 

  45. Soleimani A, Fouladi K, Araabi BN (2017) Persian offline signature verification based on curvature and gradient histograms. In: International Conference on Computer & Knowledge Engineering. IEEE, pp. 1–6

  46. Tahir M, Akram MU, Idris MA (2016) Online signature verification using segmented local features. In: International Conference on Computing, pp. 100–105

  47. Tang L, Kang W, Fang Y (2018) Information Divergence-Based Matching Strategy for Online Signature Verification. IEEE Transactions on Information Forensics and Security 13(4):861–873

    Article  Google Scholar 

  48. Tolosana R, Vera-Rodriguez R, Fierrez J, Ortega-Garcia J (2015) Feature-based dynamic signature verification under forensic scenarios. In: 2015 International workshop on biometrics and forensics (IWBF). IEEE

  49. Tolosana R, Vera-Rodriguez R, Fierrez J et al (2018) Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics. IEEE Access 6:5128–5138

    Article  Google Scholar 

  50. Vaseghi B, Hashemi S (2015) Online Signature Verification Using Vector Quantization and Hidden Markov Model. IOSR Journal of Electronics and Communication Engineering 10(2):48–53

    Google Scholar 

  51. Wang WC, Li XW, Zhi J et al (2007) Contour matching based on Hausdorff distance. Journal of Xi'an University of Posts and. Telecommunications 12(3):91–94

    Google Scholar 

  52. Xia X, Chen Z, Luan F et al (2017) Signature alignment based on GMM for on-line signature verification. Pattern Recogn 65(C):188–196

    Article  Google Scholar 

  53. Xia X, Song X, Luan F et al (2018) Discriminative feature selection for on-line signature verification. Pattern Recogn 74:422–433

    Article  Google Scholar 

  54. Yang L, Cheng YT, Wang XM, Liu Q (2018) Online handwritten signature verification using feature weighting algorithm relief. Soft Comput 22(23):7811–7823

    Article  Google Scholar 

  55. Yang L, Jin X, Jiang Q (2018) Online handwritten signature verification based on the most stable feature and partition. Cluster Computing, pp. 1–11

  56. Yeung D Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) SVC 2004: first international signature verification competition. Proceedings of the International Conference on Biometric Authentication 5:16–22

  57. Zalasiński M, Cpałka K, Hayashi Y (2015) New fast algorithm for the dynamic signature verification using global features values. In: International Conference on Artificial Intelligence & Soft Computing. Springer International Publishing, pp. 175–188

  58. Zhu YJ, Zhou LS, Wang J (2005) Contour extraction and feature point detection for 3-D fragments reassembly. Transactions of Nan Jing University of Aeronautics & Astronautics 22(1):23–29

    Google Scholar 

  59. Zhu YJ, Zhou LS, Zhang LY (2007) Algorithm for three-dimensional fragments reassembly. Journal of Image & Graphics 12(1):164–170

    Google Scholar 

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Acknowledgements

This work was supported by National College Students Innovation and entrepreneurship training program in Wuhan University of Technology (Project No. 20161049714003). The authors would like to thank the reviewers for their invaluable comments and all the people who have provided their sample signatures used in this study.

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  1. School of Science, Wuhan University of Technology, Wuhan, 430070, China

    Lang He, Hua Tan & Zhang-Can Huang

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  1. Lang He
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  2. Hua Tan
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Correspondence to Hua Tan.

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He, L., Tan, H. & Huang, ZC. Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance. Multimed Tools Appl 78, 19253–19278 (2019). https://doi.org/10.1007/s11042-019-7264-6

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  • DOI: https://doi.org/10.1007/s11042-019-7264-6

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