[1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition “(CVPR), pp. 770–778 (2016)

[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 30–31 (2017)

[3] Rexit, A., Sharma, R.K., Kushwaha, D.S.: Multilingual handwritten signature recognition based on high-dimensional feature fusion. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 1234–1239 (2019)

[4] Day, S., Halder, S., Roy, P.P.: SigNet: convolutional Siamese network for writer independent offline signature verification. In: Proceedings of the IEEE International Conference on Document Analysis and Recognition, pp. 567–572 (2018)

[5] Ren, J.-X., Xiong, Y.-J., Zhang, H., Huang, B.: 2C2S: a two-channel and two-stream transformer-based framework for offline signature verification. In: 2023 IEEE International Conference on Image Processing (ICIP) (2023)

[6] Cortes, C., Vapnik, V.: Support-vector networks. In: Proceedings of the Machine Learning Conference (MLC), pp. 123–130 (1995)

[7] Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 785–794 (2016)

[8] Bishop, C.M.: Pattern Recognition and Machine Learning (Logistic Regression). Springer (2006)

[9] John, G.H., Langley, P.: The Naive Bayes model. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI), pp. 338–343 (1999)

[10] He, K. et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

[11] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (VGG16). In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)

[12] Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks (EfficientNetB3). In: Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 610–618 (2019)

[13] Dosovitskiy, A., Beyer, L., Kolesnikov, A. et al.: An image is worth 16×16 words: transformers for image recognition (ViT-B16). In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6318–6328 (2021)