Soft Computing

, Volume 23, Issue 2, pp 407–418 | Cite as

Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures

  • Rami Al-HmouzEmail author
  • Witold Pedrycz
  • Khaled Daqrouq
  • Ali Morfeq
  • Ahmed Al-Hmouz
Methodologies and Application


One of the multimodal biometric scenarios is realized by considering several features coming from a single biometric entity. Dynamic signature verification has been utilized considering such scenarios. We present a new approach, namely probabilistic dynamic time warping, to verify dynamic signatures where we use dynamic time warping in realizing distance determination in the verification process. Signatures are segmented into several segments, where probability of each segment is quantified with the aid of a relative distance associated with two selected threshold levels. The final decision is achieved by combining all segment probabilities using a Bayes rule. Experiments demonstrate improvement of equal error rate for the proposed approach for the random forgery. The method has been tested on synthetic dataset and two publicly available databases of dynamic signatures, namely SCV2004 and MCYT100.


Multimodal identification Dynamic signature Dynamic time warping 



This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH)—King Abdulaziz City for Science and Technology—the Kingdom of Saudi Arabia—award number (12-INF3105-03). The authors also acknowledge with thanks Science and Technology Unit, King Abdulaziz University, for technical support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Banko Z, Janos A (2012) Correlation based dynamic time warping of multivariate time series. Exp Syst Appl 39:12814–12823CrossRefGoogle Scholar
  2. Bellman R (1957) Dynamic programming. Princeton University Press, PrincetonzbMATHGoogle Scholar
  3. Cpalka K, Zalasiński M (2014) On-line signature verification using vertical signature partitioning. Exp Syst Appl 41:4170–4180CrossRefGoogle Scholar
  4. Cpalka K, Zalasiński M, Rutkowski L (2014) New method for the on-line signature verification based on horizontal partitioning. Patt Recognit 47:2652–2661CrossRefGoogle Scholar
  5. Elgarrai Z, Elmeslouhi O, Kardouchi M, Allali H, Selouani S-A (2016) Offline face recognition system based on gabor fisher descriptors and Hidden Markov Models. Int J Interact Multimed Artif Intell 4(1):11–14Google Scholar
  6. Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) HMM-based on-line signature verification: feature extraction and signature modeling. Patt Recognit Lett 28(16):2325–2334CrossRefGoogle Scholar
  7. Fierrez-Aguilar J, Nanni L, Lopez-Penalba J, Ortega-Garcia J, Maltoni D (2005) An on-line signature verification system based on fusion of local and global information. In: Kanade T, Jain A, Ratha NK (eds) AVBPA 2005, vol 3546. LNCS. Springer, Heidelberg, pp 523–532Google Scholar
  8. Fierrez-Aguilar J, Krawczyk S, Ortega-Garcia J, Jain A.K (2005) Fusion of local and regional approaches for on-line signature verification. In: S.Z Li , Sun Z, Tan T, Pankanti S, Chollet G, Zhang D (eds.) IWBRS 2005. LNCS Springer, Heidelberg, vol. 3781, pp. 188–196Google Scholar
  9. Garcia-Salicetti S, Fierrez-Aguilar J, Alonso-Fernandez F, Vielhauer C, Guest R, Allano L, Trung TD, Scheidat T, Van BL, Dittmann J, Dorizzi B, Ortega-Garcia J, Gonzalez-Rodriguez J, di MB, Castiglione M, Fairhurst M (2007) Biosecure reference systems for on-line signature verification: a study of complementarity. Ann Telecommun Spec Issue on Multim Biom 62(1–2):36–61Google Scholar
  10. Humm A, Hennebert J, Ingold R (2009) Combined handwriting and speech modalities for user authentication in systems. IEEE Trans Man and Cybern Part A: Syst Hum 39(1):25–35CrossRefGoogle Scholar
  11. Jain A, Griess F, Connell S (2002) On-line signature verification. Patt Recognit 35(12):2963–2972CrossRefzbMATHGoogle Scholar
  12. Jain A, Ross A, Pankanti S (2006) Biometrics: a tool for information security. IEEE Trans Inform Forensics and Sec 1(2):125–143CrossRefGoogle Scholar
  13. Jalal A, Kamal S, Kim D (2017) A depth video-based human detection and activity recognition using multi-features and embedded Hidden Markov Models for health care monitoring systems. Int J Interact Multimed Artif Intell 4(4):54–62Google Scholar
  14. Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Patt Recognit 44:2231–2240CrossRefGoogle Scholar
  15. Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Patt Recognit 44:2231–2240CrossRefGoogle Scholar
  16. Kennedy J, Eberhart RC, Shi Y (2001) Swarm Intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  17. Kennedy J, Eberhart R (1995) Particle swarm optimization in neural networks. Proceedings IEEE International Conference on 1995, vol. 4, pp. 1942–1948Google Scholar
  18. Ketabdar H, Richiardi J, Drygajlo A (2005)Global feature selection for on-line signature verification. In: Proceedings of 12th international graphonomics society conference, Italy, pp. 59–63Google Scholar
  19. Maiorana E (2010) Biometric cryptosystem using function based on-line signature recognition. Exp Syst Appl 37:3454–3461CrossRefGoogle Scholar
  20. Nanni L (2006) Experimental comparison of one-class classifiers for on-line signature verification. Neurocomputing 69(7):869–873CrossRefGoogle Scholar
  21. Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, Faundez-Zanuy M, Espinosa V, Satue A, Hernaez I, Igarza JJ, Vivaracho C, Escudero D, Moro Q.I, (2003) MCYT baseline corpus: a bimodal biometric database. In: Proceedings vision, image and signal processing, IEE, vol. 150, no. 6, pp. 395–401Google Scholar
  22. Pascual-Gaspar J, Cardenoso-Payo V, Vivaracho-Pascual CE (2009) Practical on-line signature verification, lecture notes in computer science. Adv Biom 5558:1180–1189CrossRefGoogle Scholar
  23. Rashidi S, Fallah A, Towhidkhah F (2012) Feature extraction based DCT on dynamic signature verification. Sci Iranica 19(6):1810–1819CrossRefGoogle Scholar
  24. Ratan AL, Grimson WE, Wells WM (2000) Object detection and localization by dynamic template warping. Int J Comput Vision 36(2):131–147CrossRefGoogle Scholar
  25. Ross A, Nandakumar K, Jain AK (2006) Handbook of multibiometrics, 1st edn. Springer, New YorkGoogle Scholar
  26. Saylor M (2012) The mobile wave: how mobile intelligence will change everything. Perseus Books/Vanguard Press, New yorkGoogle Scholar
  27. Scheirer WJ, Rocha A, Micheals R, Boult TE (2010) Robust fusion: extreme value theory for recognition score normalization. ECCV 2010:481–495Google Scholar
  28. Scheirer WJ, Rocha A, Micheals R, Boult TE (2011) Meta-recognition: the theory and practice of recognition score analysis. Patt Anal Mach Intell IEEE Trans 33(8):1689–1695CrossRefGoogle Scholar
  29. Srivastava S, Bhardwaj S, Bhargava S (2016) Fusion of palm-phalanges print with palmprint and dorsal hand vein. Appl Soft Comput 47:12–20CrossRefGoogle Scholar
  30. Xiao Q, Siqi L (2017) Motion retrieval based on dynamic Bayesian network and canonical time warping. Soft Comput 21(1):267–280CrossRefGoogle Scholar
  31. Yeung DY, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) SVC 2004: first international signature verification competition. In: Proceedings of the international conference on biometric authentication, LNCS, 3072, pp. 16–22Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Information TechnologyMiddle East UniversityAmmanJordan

Personalised recommendations