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A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems

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Abstract

With the advancement in technology, the society demands a robust method for person authentication. Traditional authentication methods are based on the person’s knowledge such as PIN, passwords, and tokens etc. However, such methods are prone to steal and forgotten risks. Therefore, an efficient method for person identification and verification is required. In this paper, we present a novel biometric approach for online handwritten signature recognition and verification using Dempster–Shafer theory (DST). DST has been used effectively for combination of different information sources which provide incomplete, and complementary knowledge. Initially, signature identification and verification processes have been carried out using two different classifiers, namely, Hidden Markov Model (HMM) and Support Vector Machine (SVM). Next, the performance in terms of accuracy and the reliability of the system has been increased using DST by combining the probabilistic outputs of SVM and HMM classifiers. The feasibility of the approach has been tested on MCYT DB1 and SVC2004 biometric public databases for Latin script and a new online signature dataset for Devanagari script. To our knowledge there exist no dataset on online signature available in Devanagari script. Experimental results shows that the present approach is efficient in recognition and verification of signatures and outstrips existing work in this regard till date.

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  1. https://sites.google.com/site/iitrcsepradeep7/.

References

  1. Aritra D, Ghosh S, Sarkhel R, Choudhuri S, Das N, Nasipuri M (2018) Combining multi-level contexts of superpixel using convolutional neural networks to perform natural scene labeling. In: Second international conference on computing and communication 2018, Sikkim Manipal Institute of Technology, Sikkim. Springer, pp 1–9

  2. Bouguelia MR, Nowaczyk S, Santosh K, Verikas A (2018) Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int J Mach Learn Cybern 9(8):1307–1319

    Article  Google Scholar 

  3. Cpałka K, Zalasiński M, Rutkowski L (2014) New method for the on-line signature verification based on horizontal partitioning. Pattern Recognit 47(8):2652–2661

    Article  Google Scholar 

  4. Diaz M, Fischer A, Ferrer M, Plamondon R (2016) Dynamic signature verification system based on one real signature. IEEE Trans Cybern 99:1–12

    Google Scholar 

  5. Dubois D, Prade H, Smets P (2001) New semantics for quantitative possibility theory. In: European conference on symbolic and quantitative approaches to reasoning and uncertainty. Springer, pp 410–421

  6. Emerich S, Lupu E, Rusu C (2010) On-line signature recognition approach based on wavelets and support vector machines. In: International conference on automation quality and testing robotics (AQTR), vol 3. IEEE, pp 1–4

  7. Faundez-Zanuy M, Pascual-Gaspar JM (2011) Efficient on-line signature recognition based on multi-section vector quantization. Pattern Anal Appl 14(1):37–45

    Article  MathSciNet  Google Scholar 

  8. Fierrez-Aguilar J, Krawczyk S, Ortega-Garcia J, Jain AK (2005) Fusion of local and regional approaches for on-line signature verification. In: Advances in biometric person authentication. Springer, Berlin, pp 188–196

    Book  Google Scholar 

  9. Fischer A, Diaz M, Plamondon R, Ferrer MA (2015) Robust score normalization for DTW-based on-line signature verification. In: 13th international conference on document analysis and recognition (ICDAR), IEEE, pp 241–245

  10. Guru D, Prakash H (2009) Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans Pattern Anal Mach Intell 31(6):1059–1073

    Article  Google Scholar 

  11. Hinckley K, Sinclair M (1999) Touch-sensing input devices. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 223–230

  12. Jaeger S, Manke S, Reichert J, Waibel A (2001) Online handwriting recognition: the npen++ recognizer. Int J Doc Anal Recognit 3(3):169–180

    Article  Google Scholar 

  13. Kaur B, Singh D, Roy PP: A novel framework of EEG-based user identification by analyzing music-listening behavior. Multimedia tools and applications, pp 1–22

  14. Kessentini Y, Burger T, Paquet T (2015) A dempster-shafer theory based combination of handwriting recognition systems with multiple rejection strategies. Pattern Recognit 48(2):534–544

    Article  Google Scholar 

  15. Khazaee M, Ahmadi H, Omid M, Moosavian A, Khazaee M (2014) Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on dempster-shafer evidence theory. Proc Inst Mech Eng Part E J Process Mech Eng 228(1):21–32

    Article  Google Scholar 

  16. Kumar P, Saini R, Roy PP, Dogra DP (2016) Study of text segmentation and recognition using leap motion sensor. IEEE Sens J 17(5):1293–1301

    Article  Google Scholar 

  17. Kumar P, Saini R, Roy PP, Dogra DP (2017) 3D text segmentation and recognition using leap motion. Multimed Tools Appli 76(15):16491–16510

    Article  Google Scholar 

  18. Kumar P, Saini R, Roy PP, Dogra DP (2017) A bio-signal based framework to secure mobile devices. J Netw Comput Appl 89:62–71

    Article  Google Scholar 

  19. Lee J, Yoon HS, Soh J, Chun BT, Chung YK (2004) Using geometric extrema for segment-to-segment characteristics comparison in online signature verification. Pattern Recognit 37(1):93–103

    Article  MATH  Google Scholar 

  20. Lejtman DZ, George SE (2001) On-line handwritten signature verification using wavelets and back-propagation neural networks. In: Sixth international conference on document analysis and recognition. IEEE, pp 992–996

  21. Liu Y, Yang Z, Yang L (2015) Online signature verification based on DCT and sparse representation. IEEE Trans Cybern 45(11):2498–2511

    Article  Google Scholar 

  22. Liwicki M, Malik MI, van den Heuvel CE, Chen X, Berger C, Stoel R, Blumenstein M, Found B (2011) Signature verification competition for online and offline skilled forgeries (sigcomp2011). In: International conference on document analysis and recognition (ICDAR). IEEE, pp 1480–1484

  23. López-García M, Ramos-Lara R, Miguel-Hurtado O, Cantó-Navarro E (2014) Embedded system for biometric online signature verification. IEEE Trans Ind Inf 10(1):491–501

    Article  Google Scholar 

  24. Nanni L, Lumini A (2005) Ensemble of parzen window classifiers for on-line signature verification. Neurocomputing 68:217–224

    Article  Google Scholar 

  25. Ortega-Garcia J, Fiérrez-Aguilar J, Martin-Rello J, Gonzalez-Rodriguez J (2003) Complete signal modeling and score normalization for function-based dynamic signature verification. In: Audio-and video-based biometric person authentication. Springer, pp 1058–1058

  26. Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, Faundez-Zanuy M, Espinosa V, Satue A, Hernaez I, Igarza JJ, Vivaracho C (2003) Mcyt baseline corpus: a bimodal biometric database. IEE Proc Vis Image Signal Proces 150(6):395–401

    Article  Google Scholar 

  27. Parizeau M, Plamondon R (1990) A comparative analysis of regional correlation, dynamic time warping, and skeletal tree matching for signature verification. IEEE Trans Pattern Anal Mach Intell 12(7):710–717

    Article  Google Scholar 

  28. Parodi M, Gómez JC (2014) Legendre polynomials based feature extraction for online signature verification. Consistency analysis of feature combinations. Pattern Recognit 47(1):128–140

    Article  Google Scholar 

  29. Platt J (1999) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Advances in large margin classifiers, pp 40–61

  30. Roy PP, Dey P, Roy S, Pal U, Kimura F (2014) A novel approach of bangla handwritten text recognition using hmm. In: 14th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 661–666

  31. Rúa EA, Castro JLA (2012) Online signature verification based on generative models. IEEE Trans Syst Man Cybern Part B (Cybern) 42(4):1231–1242

    Article  Google Scholar 

  32. Dutta S, Saini RPK, Roy PP (2017) An efficient approach for recognition and verification of on-line signatures using PSO. In: 4th Asian conference on pattern recognition (ACPR) (Accepted)

  33. Sae-Bae N, Memon N (2014) Online signature verification on mobile devices. IEEE Trans Inf Forensics Secur 9(6):933–947

    Article  Google Scholar 

  34. Santosh K, Lamiroy B, Wendling L (2013) Dtw-radon-based shape descriptor for pattern recognition. Int J Pattern Recognit Artif Intell 27(03):1350,008

    Article  MathSciNet  Google Scholar 

  35. Santosh K, Nattee C, Lamiroy B (2010) Spatial similarity based stroke number and order free clustering. In: 12th international conference on frontiers in handwriting recognition. IEEE, pp 652–657

  36. Santosh K, Nattee C, Lamiroy B (2012) Relative positioning of stroke-based clustering: a new approach to online handwritten devanagari character recognition. Int J Image Gr 12(02):1250,016

    Article  MathSciNet  Google Scholar 

  37. Santosh K, Wendling L (2015) Character recognition based on non-linear multi-projection profiles measure. Front Comput Sci 9(5):678–690

    Article  Google Scholar 

  38. Sharma A, Sundaram S (2018) On the exploration of information from the DTW cost matrix for online signature verification. IEEE Trans Cybern 48(2):611–624

    Article  Google Scholar 

  39. Tolosana R, Vera-Rodriguez R, Ortega-Garcia J, Fierrez J (2015) Preprocessing and feature selection for improved sensor interoperability in online biometric signature verification. IEEE Access 3:478–489

    Article  Google Scholar 

  40. Vajda S, Santosh K (2016) A fast k-nearest neighbor classifier using unsupervised clustering. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 185–193

  41. Wu QZ, Lee SY, Jou IC (1998) On-line signature verification based on logarithmic spectrum. Pattern Recognit 31(12):1865–1871

    Article  Google Scholar 

  42. Xinghua X, Xiaoyu S, Fangun L, Jungang Z, Zhili C, Xiaofu M (2018) Discriminative feature selection for on-line signature verification. Pattern Recognit 74:422–433

    Article  Google Scholar 

  43. Xinghua X, Zhili C, Fangjun L, Xiaoyu S (2017) Signature alignment based on gmm for on-line signature verification. Pattern Recognit 65:188–196

    Article  Google Scholar 

  44. Yeung D.Y, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) Svc2004: first international signature verification competition. In: Biometric authentication. Springer, pp 16–22

  45. Zadeh LA (1986) A simple view of the dempster-shafer theory of evidence and its implication for the rule of combination. AI Mag 7(2):85

    Google Scholar 

  46. Wang X, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  47. Wang XZ, He YL, Wang DD (2014) Non-naive bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39

    Article  Google Scholar 

  48. Wang XZ, Wang R, Feng HM, Wang HC (2014) A new approach to classifier fusion based on upper integral. IEEE Trans Cybern 44(5):620–635

    Article  MathSciNet  Google Scholar 

  49. Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715

    Article  MathSciNet  Google Scholar 

  50. Wang XZ, Zhang T, Wang R (2017) Noniterative deep learning: incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst 99:1–10

    Google Scholar 

  51. Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Patna, Patna, India

    Rajib Ghosh

  2. Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India

    Pradeep Kumar & Partha Pratim Roy

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  1. Rajib Ghosh
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  2. Pradeep Kumar
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  3. Partha Pratim Roy
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Correspondence to Pradeep Kumar.

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Ghosh, R., Kumar, P. & Roy, P.P. A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems. Int. J. Mach. Learn. & Cyber. 10, 2467–2482 (2019). https://doi.org/10.1007/s13042-018-0883-9

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