References
- E. Eidinger, R. Enbar, and T. Hassner, "Age and gender estimation of unfiltered faces," IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2170-2179, 2014. https://doi.org/10.1109/TIFS.2014.2359646
- G. Levi and T. Hassner, "Age and gender classification using convolutional neural networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, 2015, pp. 34-42.
- A. Dehghan, E. G. Ortiz, G. Shu, and S. Z. Masood, "DAGER: deep age, gender and emotion recognition using convolutional neural network," 2017 [Online]. https://arxiv.org/abs/1702.04280.
- S. Lapuschkin, A. Binder, K. R. Muller, and W. Samek, "Understanding and comparing deep neural networks for age and gender classification," in Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 2017, pp. 1629-1638.
- K. Zhang, C. Gao, L. Guo, M. Sun, X. Yuan, T. X. Han, Z. Zhao, and B. Li, "Age group and gender estimation in the wild with deep RoR architecture," IEEE Access, vol. 5, pp. 22492-22503, 2017. https://doi.org/10.1109/ACCESS.2017.2761849
- 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.
- Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrel, "Caffe: convolutional architecture for fast feature embedding," in Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, 2014, pp. 675-678.
- 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. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 2015 [Online]. Available: https://arxiv.org/abs/1409.1556.
- O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., "ImageNet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, pp. 211-252, 2015. https://doi.org/10.1007/s11263-015-0816-y
- R. Rothe, R. Timofte, and L. Van Gool, "Deep expectation of real and apparent age from a single image without facial landmarks," International Journal of Computer Vision, vol. 126, pp. 144-157, 2018. https://doi.org/10.1007/s11263-016-0940-3
- Wikipedia, "softmas function," 2020 [Online]. Available: https://en.wikipedia.org/wiki/Softmax_function.
- Wikipedia, "Convolutional neural network,", 2020 [Online]. Available: https://en.wikipedia.org/wiki/Convolutional_neural_network.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2015 [Online]. Available: https://arxiv.org/abs/1512.03385.
- A. Thomas, "An introduction to Global Average Pooling in convolutional neural networks," 2019 [Online]. Available: https://adventuresinmachinelearning.com/global-average-pooling-convolutional-neural-networks/.
- G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017, pp. 4700-4708.
- Wikipedia, "Arg max," 2020 [Online]. Available: https://en.wikipedia.org/wiki/Arg_max.
- A. Singh, "A comprehensive guide to ensemble learning (with Python codes)," 2018 [Online]. Available: https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/.
- R. R. Picard and R. D. Cook, "Cross-validation of regression models," Journal of the American Statistical Association, vol. 79, no. 387, pp. 575-583, 1984. https://doi.org/10.1080/01621459.1984.10478083
- S. Arlot and A. Celisse, "A survey of cross-validation procedures for model selection," Statistics Surveys, vol. 4, pp. 40-79, 2010. https://doi.org/10.1214/09-SS054