Document Type : Research Paper
Authors
1 Department of Applied Mathematics, Faculty of Sciences and Modern Technology, Graduate University of Advanced Technology, Kerman, Iran.
2 Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.
3 Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran.
4 Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
5 Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Abstract
Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten signatures are composed of curves and the performance of a signature verification/recognition system is directly related to the edge structures, subbands of shearlet transform of signature images are good candidates for input information to the system. Furthermore, by using transfer learning of some pre-trained models, appropriate features would be extracted. In this study, four pre-trained models have been used: SigNet and SigNet-F (trained on offline signature datasets), VGG16 and VGG19 (trained on ImageNet dataset). Experiments have been conducted using three datasets: UTSig, FUM-PHSD and MCYT-75. Obtained experimental results, in comparison with the literature, verify the effectiveness of the presented method in both signature verification and signature recognition.
Keywords
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[25] K. Guo and D. Labate, The construction of smooth parseval frames of shearlets, Math. Model. Nat. Phenom., 8 (2013), pp. 82-105.
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[43] G. Kutyniok, W.Q. Lim and R. Reisenhofer, Shearlab 3D: faithful digital shearlet transforms based on compactly supported shearlets, J. ACM Trans. Math. Softw., 42 (2016), pp. 1-42.
[44] G. Kutyniok, M. Shahram and X. Zhuang, ShearLab: a rational design of a digital parabolic scaling algorithm, SIAM J. Imaging Sci., 5 (2012), pp. 1291-1332.
[45] S. Mallat and S. Zhong, Characterization of signals from multiscale edges, IEEE Trans. Pattern Anal. Mach. Intell., 14 (1992), pp. 710-732.
[46] S. Min, B. Lee and S. Yoon, Deep learning in bioinformatics, Briefings in Bioinformatics, 18 (2016), pp. 851-869.
[47] R.A. Mohammed, R.M. Nabi, S.M.R. Mahmood and R.M. Nabi, State-of-the-art in handwritten signature verification system, Int. Conf. Comput. Sci. Comput. Intell., (2015), pp. 519-525.
[48] P.N. Narwade, R.R. Sawant and S.V. Bonde, Offline handwritten signature verification using cylindrical shape context, 3D Res. 9 (2018), https://doi.org/10.1007/s13319-018-0200-0.
[49] S.Y. Ooi, A.B. J. Teoh, Y.H. Pang and B.Y. Hiew, Image-based handwritten signature verification using hybrid methods of discrete radon transform, principal component analysis and probabilistic neural network, Appl. Soft Comput., 40 (2016), pp. 274-282.
[50] N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Sys. Man. Cyber., 9 (1979), pp. 62-66.
[51] S.J. Pan and Q. Yang, A survey on transfer learning, IEEE Trans. Knowledge Data Eng., 22 (2010), pp. 1345-1359.
[52] D.N. Perkins and G. Salomon, Transfer of learning, Int. Encyclopedia of Education, 2nd Edition, Oxford, England: Pergamon Press, 1992.
[53] M.R. Pourshahabi, M.H. Sigari and H.R. Pourreza, Offline handwritten signature identification and verification using contourlet transform, Int. Conf. Soft Comput. Pattern Recognit., (2009), pp. 670-673.
[54] L.Y. Pratt, Discriminability-based transfer between neural networks, Advances in Neural Inf. Process. Syst., 5 (1993), pp. 204-211.
[55] S. Rajaraman et al., Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images, (2018), PeerJ 6:e4568; DOI: 10.7717/peerj.4568.
[56] W. Rawat and Z. Wang, Deep convolutional neural networks for image classification: a comprehensive review, Neural Comput., 29 (2017), pp. 2352-2449.
[57] S. Razavian, H. Azizpour, J. Sullivan and S. Carlsson, CNN features off-the-shelf: an astounding baseline for recognition, IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), (2014), pp. 512-519.
[58] J.D. Regele and O.V. Vasilyev, An adaptive wavelet-collocation method for shock computations, Int. J. Comput. Fluid Dyn., 23 (2009), pp. 503-518.
[59] J. Sadri, M.J. Jalili, Y. Akbari and A. Foroozandeh, Designing a new standard structure for improving automatic processing of Persian handwritten bank cheques, Int. J. Pattern Anal. Appl. (PAA), 17 (2014), pp .849-862.
[60] M.H. Saffar, M. Fayyaz, M. Sabokrou and M. Fathy, Online signature verification using deep representation: a new descriptor, Int. Comput. Vis. Pattern Recognit. (cs.CV), arXiv:1806.09986.
[61] L. Samuel, Some studies in machine learning using the game of checkers, IBM J. Res. Dev., 3 (1959), pp. 210-229.
[62] J. Schmidhuber, Deep learning in neural networks: an overview, Neural Networks, 61 (2015), pp. 85-117.
[63] M. Sharif, M.A. Khan, M. Faisal, M. Yasmin and S.L. Fernandes, A framework for offline signature verification system: best features selection approach, Pattern Recognit. Lett., 2018.
[64] M.H. Sigari, M.R. Pourshahabi and H. R. Pourreza, Offline handwritten signature identification and verification using multi-resolution Gabor wavelet, Int. J. Biometrics and Bioinform. (IJBB), 5 (2011), pp. 234-248.
[65] K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv 1409.1556, 2014.
[66] A. Soleimani, B.N. Araabi and K. Fouladi, Deep multitask metric learning for offline signature verification, Pattern Recognit. Lett., 80 (2016), pp. 84-90.
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