References
- N. Crosswhite, J. Byrne, C. Stauffer, O. Parkhi, Q. Cao, and A. Zisserman, "Template adaptation for face verification and identification," Image and Vision Computing, vol. 79, pp. 35-48, 2018. https://doi.org/10.1016/j.imavis.2018.09.002
- X. X. Niu and C. Y. Suen, "A novel hybrid CNN-SVM classifier for recognizing handwritten digits," Pattern Recognition, vol. 45, no. 4, pp. 1318-1325, 2012. https://doi.org/10.1016/j.patcog.2011.09.021
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105, 2012.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, 2015.
- D. Matthew and R. Fergus, "Visualizing and understanding convolutional neural networks," in Computer Vision - ECCV 2014. Cham, Switzerland: Springer, 2014, pp. 818-833.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 1-9.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
- G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017, pp. 1243-1252.
- F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: a unified embedding for face recognition and clustering," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 815-823.
- Y. Sun, X. Wang, and X. Tang, "Deep learning face representation from predicting 10,000 classes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1891-1898.
- B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, and A. K. Jain, "Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus benchmark A," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 1931-1939.
- P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, "Overfeat: integrated recognition, localization and detection using convolutional networks," in Proceedings of the 2nd International Conference on Learning Representations, Banff, Canada, 2014.
- Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "VGGFace2: a dataset for recognising faces across pose and age," in Proceedings of 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG), Xi'an, China, 2018, pp. 67-74.
- C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 2818-2826.
- M. Mavronicolas and V. G. Papadopoulou, Algorithmic Game Theory. Heidelberg, Germany: Springer, 2009. https://doi.org/10.1007/978-3-642-04645-2
- C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 2818-2826.
- C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, "Inception-v4, Inception-ResNet and the impact of residual connections on learning," 2016 [Online]. Available: https://arxiv.org/abs/1602.07261.