[
Ahrabian, K. and BabaAli, B. (2019). Usage of autoencoders and siamese networks for online handwritten signature verification, Neural Computing and Applications 31(12): 9321–9334.
]Search in Google Scholar
[
Arab, N., Nemmour, H. and Chibani, Y. (2023). A new synthetic feature generation scheme based on artificial immune systems for robust offline signature verification, Expert Systems with Applications 213(Part C): 119306.
]Search in Google Scholar
[
Avola, D., Bigdello, M.J., Cinque, L., Fagioli, A. and Marini, M.R. (2021). R-sigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification, Pattern Recognition Letters 150(C): 189–196.
]Search in Google Scholar
[
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E. and Shah, R. (1994). Signature verification using a “siamese” time delay neural network, in J. Cowan et al. (Eds), Advances in Neural Information Processing Systems, Morgan Kaufmann, San Francisco, USA, pp. 737–744.
]Search in Google Scholar
[
Calik, N., Kurban, O.C., Yilmaz, A.R., Yildirim, T. and Ata, L.D. (2019). Large-scale offline signature recognition via deep neural networks and feature embedding, Neurocomputing 359(C): 1–14.
]Search in Google Scholar
[
Devidas, S., Rao, Y.S. and Rekha, N.R. (2021). A decentralized group signature scheme for privacy protection in a blockchain, International Journal of Applied Mathematics and Computer Science 31(2): 353–364, DOI: 10.34768/amcs-2021-0024.
]Search in Google Scholar
[
Dey, S., Dutta, A., Toledo, J.I., Ghosh, S.K., Lladós, J. and Pal, U. (2017). SigNet: Convolutional siamese network for writer independent offline signature verification, arXiv: 1707.02131.
]Search in Google Scholar
[
Diaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G. and Plamondon, R. (2019). A perspective analysis of handwritten signature technology, ACM Computing Surveys 51(6): 1–39.
]Search in Google Scholar
[
Ferrer, M.A., Diaz, M., Carmona-Duarte, C. and Morales, A. (2016). A behavioral handwriting model for static and dynamic signature synthesis, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6): 1041–1053.
]Search in Google Scholar
[
Fierrez, J., Ortega-Garcia, J., Ramos, D. and Gonzalez-Rodriguez, J. (2007). HMM-based on-line signature verification: Feature extraction and signature modeling, Pattern Recognition Letters 28(16): 2325–2334.
]Search in Google Scholar
[
Giazitzis, A. and Zois, E. (2024). SigmML: Metric meta-learning for writer independent offline signature verification in the space of SPD matrices, 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),Waikoloa, Hawaii, United States, pp. 6300–6310.
]Search in Google Scholar
[
Hafemann, L.G., Oliveira, L.S. and Sabourin, R. (2018). Fixed-sized representation learning from offline handwritten signatures of different sizes, International Journal on Document Analysis and Recognition (IJDAR) 21(3): 219–232.
]Search in Google Scholar
[
Hafemann, L.G., Sabourin, R. and Oliveira, L.S. (2017). Learning features for offline handwritten signature verification using deep convolutional neural networks, Pattern Recognition 70(C): 163–176.
]Search in Google Scholar
[
Hameed, M.M., Ahmad, R., Kiah, L.M., Murtaza, G. and Mazhar, N. (2023). OffSig-SinGAN: A deep learning-based image augmentation model for offline signature verification, Computers, Materials & Continua 76(1): 1267–1289.
]Search in Google Scholar
[
Hameed, M.M., Ahmad, R., Kiah, M.L.M. and Murtaza, G. (2021). Machine learning-based offline signature verification systems: A systematic review, Signal Processing: Image Communication 93: 116139.
]Search in Google Scholar
[
Impedovo, D. and 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.
]Search in Google Scholar
[
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv: 1502.03167.
]Search in Google Scholar
[
Ji, X., Suehiro, D. and Uchida, S. (2023). Paired contrastive feature for highly reliable offline signature verification, Pattern Recognition 144(C): 109816.
]Search in Google Scholar
[
Kalera, M.K., Srihari, S. and Xu, A. (2004). Offline signature verification and identification using distance statistics, International Journal of Pattern Recognition and Artificial Intelligence 18(07): 1339–1360.
]Search in Google Scholar
[
Kaur, H.P. and Kumar, M. (2021). Signature identification and verification techniques: State-of-the-art work, Journal of Ambient Intelligence and Humanized Computing 14: 1027–1045.
]Search in Google Scholar
[
Kholmatov, A. and Yanikoglu, B. (2005). Identity authentication using improved online signature verification method, Pattern Recognition Letters 26(15): 2400–2408.
]Search in Google Scholar
[
Kingma, D.P. and Ba, J. (2014). Adam: A method for stochastic optimization, arXiv: 1412.6980.
]Search in Google Scholar
[
Lai, S., Jin, L. and Yang, W. (2017). Online signature verification using recurrent neural network and length-normalized path signature descriptor, 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Vol. 01, Kyoto, Japan, pp. 400–405.
]Search in Google Scholar
[
Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11): 2278–2324.
]Search in Google Scholar
[
Li, H., Wei, P. and Hu, P. (2021). Static-dynamic interaction networks for offline signature verification, Proceedings of the AAAI Conference on Artificial Intelligence 35(3): 1893–1901.
]Search in Google Scholar
[
Longjam, T., Kisku, D.R. and Gupta, P. (2023). Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach, Expert Systems with Applications 214(C): 119111.
]Search in Google Scholar
[
Maaten, L.v.d. and Hinton, G. (2008). Visualizing data using t-SNE, Journal of Machine Learning Research 9(Nov): 2579–2605.
]Search in Google Scholar
[
Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M. and Zhang, D. (2023). Biometrics recognition using deep learning: A survey, Artificial Intelligence Review 56(8): 8647–8695.
]Search in Google Scholar
[
Müller, R., Kornblith, S. and Hinton, G.E. (2019). When does label smoothing help?, in H.M. Wallach et al. (Eds), Advances in Neural Information Processing Systems, Red Hook, NY, USA, pp. 4694–4703.
]Search in Google Scholar
[
Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D. and Moro, Q.-I. (2003). MCYT baseline corpus: A bimodal biometric database, IEE Proceedings-Vision, Image and Signal Processing 150(6): 395–401.
]Search in Google Scholar
[
Parcham, E., Ilbeygi, M. and Amini, M. (2021). Cbcapsnet: A novel writer-independent offline signature verification model using a cnn-based architecture and capsule neural networks, Expert Systems with Applications 185(C): 115649.
]Search in Google Scholar
[
Paszke, A., Gross, S.,Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L. et al. (2019). PyTorch: An imperative style, high-performance deep learning library, in H. Wallach et al. (Eds), Advances in Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, pp. 8026–8037.
]Search in Google Scholar
[
Putz-Leszczynska, J. (2015). Signature verification: A comprehensive study of the hidden signature method, International Journal of Applied Mathematics and Computer Science 25(3): 659–674, DOI: 10.1515/amcs-2015-0048.
]Search in Google Scholar
[
Qiao, Y., Liu, J. and Tang, X. (2007). Offline signature verification using online handwriting registration, IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, United States, pp. 1–8.
]Search in Google Scholar
[
Radhika, K. and Gopika, S. (2015). Online and offline signature verification: A combined approach, Procedia Computer Science 46(C): 1593–1600.
]Search in Google Scholar
[
Ren, J.-X., Xiong, Y.-J., Zhan, H. and Huang, B. (2023). 2c2s: A two-channel and two-stream transformer based framework for offline signature verification, Engineering Applications of Artificial Intelligence 118(C): 105639.
]Search in Google Scholar
[
Sadak, M.S., Kahraman, N. and Uludağ, U. (2022). Dynamic and static feature fusion for increased accuracy in signature verification, Signal Processing: Image Communication 108(C): 116823.
]Search in Google Scholar
[
Sekhar V., C., Gautam, A., Pulabaigari, V., S.R., S. and Sai G., R.K. (2023). TSOSVNet: Teacher-student collaborative knowledge distillation for online signature verification, IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, pp. 742–751.
]Search in Google Scholar
[
Tolosana, R., Vera-Rodriguez, R., Fierrez, J. and Ortega-Garcia, J. (2018). Exploring recurrent neural networks for on-line handwritten signature biometrics, IEEE Access 6: 5128–5138.
]Search in Google Scholar
[
Tolosana, R., Vera-Rodríguez, R., Fíerrez, J. and Ortega-Garcia, J. (2021). Deepsign: Deep on-line signature verification, IEEE Transactions on Biometrics, Behavior, and Identity Science 3(2): 229–239.
]Search in Google Scholar
[
Touvron, H., Vedaldi, A., Douze, M. and Jégou, H. (2019). Fixing the train-test resolution discrepancy, in H. Wallach et al. (Eds), Advances in Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, pp. 8252–8262.
]Search in Google Scholar
[
Uppalapati, D. (2007). Integration of Offline and Online Signature Verification Systems, Master’s thesis, IIT Kanpur.
]Search in Google Scholar
[
Vargas, F., Ferrer, M., Travieso, C. and Alonso, J. (2007). Off-line handwritten signature GPDS-960 corpus, 9-th International Conference on Document Analysis and Recognition (ICDAR 2007), Vol. 2, Curitiba, Brazil, pp. 764–768.
]Search in Google Scholar
[
Viana, T.B., Souza, V.L., Oliveira, A.L., Cruz, R.M. and Sabourin, R. (2023). A multi-task approach for contrastive learning of handwritten signature feature representations, Expert Systems with Applications 217(C): 119589.
]Search in Google Scholar
[
Vorugunti, C., Gautam, A. and Pulabaigari, V. (2023). Osvcontramer: A hybrid cnn and transformer based online signature verification, Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), Ljubljana, Slovenia, pp. 1–10.
]Search in Google Scholar
[
Vorugunti, C.S., Devanur S.G., Mukherjee, P. and Pulabaigari, V. (2019). Osvnet: Convolutional siamese network for writer independent online signature verification, International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, pp. 1470–1475.
]Search in Google Scholar
[
Xie, L., Wu, Z., Zhang, X. and Li, Y. (2023). Synchronous spatio-temporal signature verification via fusion triplet supervised network, Engineering Applications of Artificial Intelligence 123(B): 106378.
]Search in Google Scholar
[
Xie, L., Wu, Z., Zhang, X., Li, Y. and Wang, X. (2022). Writer-independent online signature verification based on 2D representation of time series data using triplet supervised network, Measurement 197(6): 111312.
]Search in Google Scholar
[
Xiong, Y.-J., Cheng, S.-Y., Ren, J.-X. and Zhang, Y.-J. (2023). Attention-based multiple siamese networks with primary representation guiding for offline signature verification, International Journal on Document Analysis and Recognition 27(2): 195–208.
]Search in Google Scholar
[
Yılmaz, M.B. and Öztürk, K. (2019). Recurrent binary patterns and CNNs for offline signature verification, in A. Kohei et al. (Eds) Proceedings of the Future Technologies Conference, San Francisco, United States, pp. 417–434.
]Search in Google Scholar
[
Yılmaz, M.B. and Yanıkoğlu, B. (2016). Score level fusion of classifiers in off-line signature verification, Information Fusion 32(B): 109–119.
]Search in Google Scholar
[
Yu, H. and Shi, P. (2023). A novel deep ensemble framework for online signature verification using temporal and spatial representation, in D. Wang et al. (Eds), Information and Communications Security, Springer, Singapore, pp. 534–549.
]Search in Google Scholar
[
Yılmaz, M.B. and Öztürk, K. (2018). Hybrid user-independent and user-dependent offline signature verification with a two-channel CNN, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, United States, pp. 526–534.
]Search in Google Scholar
[
Zagoruyko, S. and Komodakis, N. (2015). Learning to compare image patches via convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, United States, pp. 4353–4361.
]Search in Google Scholar
[
Zhang, X., Wang, Y., Sun, W., Cui, Q. and Wei, X. (2023). Multi-path attention inverse discrimination network for offline signature verification, Intelligent Automation & Soft Computing 36(3): 3057–3071.
]Search in Google Scholar
[
Zimmer, A. and Ling, L.L. (2008). Offline signature verification system based on the online data, EURASIP Journal on Advances in Signal Processing 2008(1): 492910.
]Search in Google Scholar
[
Zois, E.N., Alexandridis, A. and Economou, G. (2019). Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets, Expert Systems with Applications 125(C): 14–32.
]Search in Google Scholar
[
Zois, E.N., Said, S., Tsourounis, D. and Alexandridis, A. (2023). Subscripto multiplex: A Riemannian symmetric positive definite strategy for offline signature verification, Pattern Recognition Letters 167: 67–74.
]Search in Google Scholar
[
Zois, E.N., Theodorakopoulos, I., Tsourounis, D. and Economou, G. (2017). Parsimonious coding and verification of offline handwritten signatures, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, United States, pp. 636–645.
]Search in Google Scholar
