|| Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi: 10.1109/5.726791.|
 Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS.
 C. Szegedy et al., "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1-9, doi: 10.1109/CVPR.2015.7298594.
 Ioffe, Sergey and Szegedy, Christian. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.." CoRR abs/1502.03167 (2015).
 C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 2818-2826, doi: 10.1109/CVPR.2016.308.
 Szegedy, Christian, Ioffe, Sergey and Vanhoucke, Vincent. "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.." CoRR abs/1602.07261 (2016).
 Simonyan, Karen and Andrew Zisserman. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” CoRR abs/1409.1556 (2015): n. pag.
 VGGNet. Retrieved June 14, 2020, from https://www. itread01.com/content/1568289844.html
 He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
 Huang, G., Liu, Z., & Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261-2269.
 Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J. & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size (cite arxiv:1602.07360Comment: In ICLR Format)
 Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (cite arxiv:1704.04861)
 M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 4510-4520, doi:10.1109/CVPR.2018.00474.
 MobileNetV2. Retrieved June 14, 2020, from https://firstname.lastname@example.org/%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-mobilenet-depthwise-separable-convolution-f1ed016b3467
 J. Daugman, “How Iris Recognition Works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, JAN 2004.
 J. Daugman, “Probing the Uniqueness and Randomness of IrisCodes: Results from 200 Billion Iris Pair Comparisons”, Proceedings of the IEEE, Vol 94, Issue 11, pp. 1927-1935, IEEE, November 2006.
 J. Daugman, “New Methods in Iris Recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol 37, Issue 5, pp. 1167-1175, January 2007.
 H. Hofbauer, F. A.-Fernandez, J. Bigun, and A. Uhl, “Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate,” The Institution of Engineering and Technology Biometrics, vol. 5, no. 3, pp. 200-211, AUG 2016.
 Po-Jen Huang, “A Fast Iris Segmentation Algorithm based on Faster R-CNN”, https://ndltd.ncl.edu.tw/cgi-in/gs32/gsweb.cgi/ccd=VOW3dO/record?r1=1&h1=2
 S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, JAN 2016.
 Y.-H. Li and P.-J. Huang, “An Accurate and Efficient User Authentication Mechanism on Smart Glasses based on Iris Recognition,” Mobile Information Systems, vol. 2017, Article ID 1281020, pp. 1-14, JUL 2017.
 MobileNet. Retrieved June 14, 2020, from https://zhuanlan.zhihu.com/p/54425450