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A two-tier ensemble approach for writer dependent online signature verification

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Abstract

Biometric verification systems are used to recognize people based on their uniqueness or characteristics. Signature is considered as one of the most commonly used biometric that individualizes a human being. It is generally used to keep individual’s privacy in many places such as banking sectors, academic institutes, office premises and trading. But increase of criminal attempts in falsifying an individual’s signature, known as signature forgeries, motivates the researchers to develop computerized systems that can verify the genuineness of a questioned signature. Though many researches have been performed till date, but the issue of identifying skilled forgeries still remains a major concern for the researchers. To this end, in this work, we have designed a two-tier ensemble based writer dependent and language- invariant online signature verification system. In doing so, we have first extracted three different categories of features from each input signature: physical, frequency based and statistical, and then designed a feature-classifier based ensemble (i.e., Ensemble#1) using seven different classifiers. The predictions obtained from the seven classifiers are combined using normalised distribution summation strategy. Decisions obtained from Ensemble#1 are then fed to Ensemble#2, where a majority voting based approach is followed, to identify the input signature as genuine or forged. Our system is evaluated on two standard datasets: SVC 2004 (Task-II) and MCYT-100 in a writer dependent way. The equal error rate (ERR) and accuracy on SVC 2004 dataset are 2.20 and 98.43% respectively, and on MCYT-100 dataset these are 2.84 and 97.87% respectively. The GAR@0.01FAR value obtained for the SVC-2004 dataset is 94.50% while it is 92.90% for MCYT-100 dataset. We have also compared our results with some state-of-the-art methods, and it has been found that our method performs better than most of these methods. The code of this work is available at: https://github.com/prat1999/Online_Signature_Verification.

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Data Availability Statement

The link for accessing the SVC-2004 dataset is https://www.cse.ust.hk/svc2004/ and the link for accessing the MCYT-100 dataset is http://atvs.ii.uam.es/atvs/mcyt100s.html.

References

  • Adithya DR, VL A, MR N, N S, Aditya SK (2019) Signature analysis for forgery detection. In: Shetty NR, Patnaik LM, Nagaraj HC, Hamsavath PN, Nalini N (eds) Emerging research in computing, information, communication and applications. Springer, New York, pp 339–349

  • An TK, Kim MH (2010) A new diverse adaboost classifier. In: 2010 International conference on artificial intelligence and computational intelligence, vol 1. IEEE, pp 359–363

  • Bose SSC, Sivanandam N, Sundar PVP (2020) Design of ensemble classifier using statistical gradient and dynamic weight LogitBoost for malicious tumor detection. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02295-2

    Article  Google Scholar 

  • Chang WD, Shin J (2008) Dpw approach for random forgery problem in online handwritten signature verification. In: 2008 4th international conference on networked computing and advanced information management, vol 1. IEEE, pp 347–352

  • Chen X (2020) The application of neural network with convolution algorithm in western music recommendation practice. J Ambient Intell Hum Comput https://doi.org/10.1007/s12652-020-01806-5

  • Cheng Y, Qiao X, Wang X, Yu Q (2017) Random forest classifier for zero-shot learning based on relative attribute. IEEE Trans Neural Netw Learn Syst 29(5):1662–1674

    Article  MathSciNet  Google Scholar 

  • Diaz M, Ferrer MA, Impedovo D, Malik MI, Pirlo G, Plamondon R (2019) A perspective analysis of handwritten signature technology. ACM Comput Surv 51(6):1–39. https://doi.org/10.1145/3274658

    Article  Google Scholar 

  • Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874

    MATH  Google Scholar 

  • Fayyaz M, Hajizadeh\_Saffar M, Sabokrou M, Fathy M (2015) Feature representation for online signature verification. ArXiv preprint arXiv:150508153

  • Ferrer MA, Diaz M, Carmona-Duarte C, Plamondon R (2019) Generating off-line and on-line forgeries from on-line genuine signatures. In: 2019 International Carnahan conference on security technology (ICCST). IEEE, pp 1–6

  • Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn Lett 28(16):2325–2334

    Article  Google Scholar 

  • Fierrez-Aguilar J, Krawczyk S, Ortega-Garcia J, Jain AK (2005) Fusion of local and regional approaches for on-line signature verification. In: Li SZ, Sun Z, Tan T, Pankanti S, Chollet G, Zhang D (eds) Advances in biometric person authentication. IWBRS 2005. Lecture Notes in Computer Science, vol 3781. Springer, Berlin, Heidelberg, pp 188–196. https://doi.org/10.1007/11569947_24

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

  • Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163

    Article  Google Scholar 

  • Guru D, Prakash H (2008) Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans Pattern Anal Mach Intell 31(6):1059–1073

    Article  Google Scholar 

  • 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. https://doi.org/10.1109/tpami.2008.302

    Article  Google Scholar 

  • Guru D, Manjunatha K, Manjunath S (2013) User dependent features in online signature verification. In: Swamy PP, Guru DS (eds) Multimedia processing, communication and computing applications. Springer, Berlin, pp 229–240

    Chapter  Google Scholar 

  • Guru D, Manjunatha K, Manjunath S, Somashekara M (2017) Interval valued symbolic representation of writer dependent features for online signature verification. Expert Syst Appl 80:232–243

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hafemann LG, Sabourin R, Oliveira LS, (2017b) Offline handwritten signature verification—literature review. In: 2017 7th international conference on image processing theory, tools and applications (IPTA). IEEE. https://doi.org/10.1109/ipta.2017.8310112

  • He H, Tan Y, Xing J (2019a) Unsupervised classification of 12-lead ecg signals using wavelet tensor decomposition and two-dimensional gaussian spectral clustering. Knowl-Based Syst 163:392–403

    Article  Google Scholar 

  • He L, Tan H, Huang ZC (2019b) Online handwritten signature verification based on association of curvature and torsion feature with hausdorff distance. Multimed Tools Appl 78(14):19253–19278. https://doi.org/10.1007/s11042-019-7264-6

    Article  Google Scholar 

  • Hofbauer H, Uhl A (2016) Calculating a boundary for the significance from the equal-error rate. In: 2016 international conference on biometrics (ICB). IEEE, pp 1–4

  • Huang K, Yan H (2003) Stability and style-variation modeling for on-line signature verification. Pattern Recogn 36(10):2253–2270. https://doi.org/10.1016/s0031-3203(03)00126-2

    Article  MATH  Google Scholar 

  • Impedovo D, Pirlo G (2018) Automatic signature verification in the mobile cloud scenario: survey and way ahead. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/tetc.2018.2865345

  • Jahromi AH, Taheri M (2017) A non-parametric mixture of gaussian naive bayes classifiers based on local independent features. In: 2017 artificial intelligence and signal processing conference (AISP). IEEE, pp 209–212

  • Jain A, Singh SK, Singh KP (2020) Handwritten signature verification using shallow convolutional neural network. Multimed Tools Appl 79:19993–20018. https://doi.org/10.1007/s11042-020-08728-6

    Article  Google Scholar 

  • Jain AK, Griess FD, Connell SD (2002) On-line signature verification. Pattern Recogn 35(12):2963–2972

    Article  Google Scholar 

  • Jia Y, Huang L, Chen H (2019) A two-stage method for online signature verification using shape contexts and function features. Sensors 19(8):1808

    Article  Google Scholar 

  • Kar B, Mukherjee A, Dutta PK (2018) Stroke point warping-based reference selection and verification of online signature. IEEE Trans Instrum Meas 67(1):2–11. https://doi.org/10.1109/tim.2017.2755898

    Article  Google Scholar 

  • Kholmatov A, Yanikoglu B (2005) Identity authentication using improved online signature verification method. Pattern Recogn Lett 26(15):2400–2408

    Article  Google Scholar 

  • Kim JC, Chung K (2020) Neural-network based adaptive context prediction model for ambient intelligence. J Ambient Intell Hum Comput 11(4):1451–1458

    Article  Google Scholar 

  • Kittur AS, Pais AR (2020) A trust model based batch verification of digital signatures in iot. Ambient Intell Hum Comput 11(1):313–327

    Article  Google Scholar 

  • Lai S, Jin L, Yang W, (2017a) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). IEEE. https://doi.org/10.1109/icdar.2017.73

  • Lai S, Jin L, Yang W (2017b) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 1. IEEE, pp 400–405

  • Lai S, Jin L, Lin L, Zhu Y, Mao H (2020) Synsig2vec: learning representations from synthetic dynamic signatures for real-world verification. Proc AAAI Conf Artif Intell 34:735–742

    Google Scholar 

  • Liu Y, Yang Z, Yang L (2015) Online signature verification based on DCT and sparse representation. IEEE Trans Cybern 45(11):2498–2511. https://doi.org/10.1109/tcyb.2014.2375959

    Article  Google Scholar 

  • López-García M, Ramos-Lara R, Miguel-Hurtado O, Cantó-Navarro E (2013) Embedded system for biometric online signature verification. IEEE Trans Ind Inform 10(1):491–501

    Article  Google Scholar 

  • Lopez-Garcia M, Ramos-Lara R, Miguel-Hurtado O, Canto-Navarro E (2014) Embedded system for biometric online signature verification. IEEE Trans Ind Inform 10(1):491–501. https://doi.org/10.1109/tii.2013.2269031

    Article  Google Scholar 

  • Lumini A, Nanni L (2009) Ensemble of on-line signature matchers based on OverComplete feature generation. Expert Syst Appl 36(3):5291–5296. https://doi.org/10.1016/j.eswa.2008.06.069

    Article  Google Scholar 

  • Lv H, Wang W, Wang C, Zhuo Q (2005) Off-line Chinese signature verification based on support vector machines. Pattern Recogn Lett 26(15):2390–2399. https://doi.org/10.1016/j.patrec.2005.04.013

    Article  Google Scholar 

  • Malik MI, Ahmed S, Marcelli A, Pal U, Blumenstein M, Alewijns L, Liwicki M, (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. https://doi.org/10.1109/icdar.2015.7333948

  • Manjunatha KS (2015) Writer dependent online signature verification system. https://hdl.handle.net/10603/203997

  • Manjunatha K, Manjunath S, Guru D, Somashekara M (2016) Online signature verification based on writer dependent features and classifiers. Pattern Recogn Lett 80:129–136

    Article  Google Scholar 

  • Mason L, Baxter J, Bartlett PL, Frean MR (2000) Boosting algorithms as gradient descent. In: Proceedings of the 12th International Conference on Neural Information Processing System, MIT Press, Cambridge, MA, USA, pp 512–518

  • Masoudnia S, Mersa O, Araabi BN, Vahabie AH, Sadeghi MA, Ahmadabadi MN (2019) Multi-representational learning for offline signature verification using multi-loss snapshot ensemble of CNNs. Expert Syst Appl 133:317–330. https://doi.org/10.1016/j.eswa.2019.03.040

    Article  Google Scholar 

  • McClish DK (1989) Analyzing a portion of the roc curve. Med Decis Mak 9(3):190–195

    Article  Google Scholar 

  • Nanni L (2006) Experimental comparison of one-class classifiers for online signature verification. Neurocomputing 69(7–9):869–873

    Article  Google Scholar 

  • Nanni L, Lumini A (2005) Ensemble of Parzen window classifiers for on-line signature verification. Neurocomputing 68:217–224. https://doi.org/10.1016/j.neucom.2005.05.004

    Article  Google Scholar 

  • Nanni L, Maiorana E, Lumini A, Campisi P (2010) Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Syst Appl 37(5):3676–3684. https://doi.org/10.1016/j.eswa.2009.10.023

    Article  Google Scholar 

  • Nyssen E, Sahli H, Zhang K (2002) A multi-stage online signature verification system. Pattern Anal Appl 5(3):288–295

    Article  MathSciNet  Google Scholar 

  • Okawa M (2019) Template matching using time-series averaging and DTW with dependent warping for online signature verification. IEEE Access 7:81010–81019. https://doi.org/10.1109/access.2019.2923093

    Article  Google Scholar 

  • Okawa M (2020) Online signature verification using single-template matching with time-series averaging and gradient boosting. Pattern Recogn 102:107227

    Article  Google Scholar 

  • Ortega-Garcia J, Fierrez-Aguilar J, Martin-Rello J, Gonzalez-Rodriguez J (2003a) Complete signal modeling and score normalization for function-based dynamic signature verification. In: Kittler J, Nixon MS (eds) Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 658–667. https://doi.org/10.1007/3-540-44887-x_77

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

    Article  Google Scholar 

  • Pascual-Gaspar JM, Cardeñoso-Payo V, Vivaracho-Pascual CE (2009) Practical on-line signature verification. In: International conference on biometrics. Springer, pp 1180–1189

  • Pascual-Gaspar JM, Faundez-Zanuy M, Vivaracho C (2011) Fast on-line signature recognition based on vq with time modeling. Eng Appl Artif Intell 24(2):368–377

    Article  Google Scholar 

  • Pirlo G, Cuccovillo V, Diaz-Cabrera M, Impedovo D, Mignone P (2015) Multidomain verification of dynamic signatures using local stability analysis. IEEE Trans Hum-Mach Syst 45(6):805–810

    Article  Google Scholar 

  • Rashidi S, Fallah A, Towhidkhah F (2012) Feature extraction based DCT on dynamic signature verification. Sci Iran 19(6):1810–1819. https://doi.org/10.1016/j.scient.2012.05.007

    Article  Google Scholar 

  • Sae-Bae N, Memon N (2014) Online signature verification on mobile devices. IEEE Trans Inf Forensics Secur 9(6):933–947. https://doi.org/10.1109/tifs.2014.2316472

    Article  Google Scholar 

  • Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst, Man, Cybern 21(3):660–674

    Article  MathSciNet  Google Scholar 

  • Savargiv M, Masoumi B, Keyvanpour MR (2020) A new ensemble learning method based on learning automata. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01882-7

    Article  Google Scholar 

  • Sayeed S, Samraj A, Besar R, Hossen J (2010) Online hand signature verification: a review. J Appl Sci 10(15):1632–1643. https://doi.org/10.3923/jas.2010.1632.1643

    Article  Google Scholar 

  • 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 

  • Sharma A, Sundaram S (2017) On the exploration of information from the dtw cost matrix for online signature verification. IEEE Trans Cybern 48(2):611–624

    Article  Google Scholar 

  • Song X, Xia X, Luan F (2016) Online signature verification based on stable features extracted dynamically. IEEE Trans Syst, Man, Cybern: Syst 47(10):2663–2676

    Article  Google Scholar 

  • Song X, Xia X, Luan F (2017) Online signature verification based on stable features extracted dynamically. IEEE Trans Syst, Man, Cybern: Syst 47(10):2663–2676. https://doi.org/10.1109/tsmc.2016.2597240

    Article  Google Scholar 

  • Souza VLF, Oliveira ALI, Sabourin R (2018) A writer-independent approach for offline signature verification using deep convolutional neural networks features. In: 2018 7th Brazilian conference on intelligent systems (BRACIS). IEEE. https://doi.org/10.1109/bracis.2018.00044

  • Souza VL, Oliveira AL, Cruz RM, Sabourin R (2020) A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification. Expert Syst Appl 154:113397. https://doi.org/10.1016/j.eswa.2020.113397

    Article  Google Scholar 

  • Sundararajan K, Woodard DL (2018) Deep learning for biometrics. ACM Comput Surv 51(3):1–34. https://doi.org/10.1145/3190618

    Article  Google Scholar 

  • Tahir M, Akram MU, Idris MA, (2016) Online signature verification using segmented local features. In: 2016 international conference on computing, electronic and electrical engineering (ICE Cube). IEEE. https://doi.org/10.1109/icecube.2016.7495205

  • Tang L, Kang W, Fang Y (2018) Information divergence-based matching strategy for online signature verification. IEEE Trans Inf Forensics Secur 13(4):861–873. https://doi.org/10.1109/tifs.2017.2769023

    Article  Google Scholar 

  • Van BL, Garcia-Salicetti S, Dorizzi B (2007) On using the viterbi path along with hmm likelihood information for online signature verification. IEEE Trans Syst, Man, Cybern, Part B (Cybern) 37(5):1237–1247

    Article  Google Scholar 

  • Vorugunti CS, Pulabaigari V, Gorthi RKSS, Mukherjee P (2020) OSVFuseNet: online signature verification by feature fusion and depth-wise separable convolution based deep learning. Neurocomputing 409:157–172. https://doi.org/10.1016/j.neucom.2020.05.072

    Article  Google Scholar 

  • Wang W, Zhao M, Wang J (2019) Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Hum Comput 10(8):3035–3043

    Article  Google Scholar 

  • Wu X, Kimura A, Iwana BK, Uchida S, Kashino K, (2019) Deep dynamic time warping: End-to-end local representation learning for online signature verification. In: 2019 international conference on document analysis and recognition (ICDAR). IEEE. https://doi.org/10.1109/icdar.2019.00179

  • Xia X, Song X, Luan F, Zheng J, Chen Z, Ma X (2018) Discriminative feature selection for on-line signature verification. Pattern Recogn 74:422–433

    Article  Google Scholar 

  • Yang L, Cheng Y, Wang X, Liu Q (2018) Online handwritten signature verification using feature weighting algorithm relief. Soft Comput 22(23):7811–7823. https://doi.org/10.1007/s00500-018-3477-2

    Article  Google Scholar 

  • Yanikoglu B, Kholmatov A (2009) Online signature verification using Fourier descriptors. EURASIP J Adv Signal Process 2009:1–13

    Article  Google Scholar 

  • Yeung DY, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) Svc2004: First international signature verification competition. In: International conference on biometric authentication. Springer, pp 16–22

  • Yoon H, Lee J, Yang H (2002) An online signature verification system using hidden markov model in polar space. In: Proceedings 8th international workshop on frontiers in handwriting recognition. IEEE Computer Society. https://doi.org/10.1109/iwfhr.2002.1030931

  • Zalasiński M, Cpałka K, Hayashi Y (2015) New fast algorithm for the dynamic signature verification using global features values. In: Artificial intelligence and soft computing. Springer, pp 175–188. https://doi.org/10.1007/978-3-319-19369-4_17

  • Zimmerman T, Russell G, Heilper A, Smith B, Hu J, Markman D, Graham J, Drews C (2004) Retail applications of signature verification. In: Proceedings of SPIE—the international society for optical engineering, pp 5404. https://doi.org/10.1117/12.542747

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Authors and Affiliations

  1. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India

    Pratik Bhowal

  2. Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India

    Debanshu Banerjee

  3. Department of Computer Science, Asutosh College, Kolkata, India

    Samir Malakar

  4. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Ram Sarkar

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  1. Pratik Bhowal
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Correspondence to Samir Malakar.

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Bhowal, P., Banerjee, D., Malakar, S. et al. A two-tier ensemble approach for writer dependent online signature verification. J Ambient Intell Human Comput 13, 21–40 (2022). https://doi.org/10.1007/s12652-020-02872-5

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