[[1] Bahdanau, D., Cho, K. and Bengio, Y. 2014, ‘Neural machine translation by jointly learning to align and translate’, arXiv preprint arXiv:1409.0473.]Search in Google Scholar
[[2] Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J. and Kumar, N. 2013, ‘Localizing parts of faces using a consensus of exemplars’, IEEE transactions on pattern analysis and machine intelligence 35(12), 2930–2940.]Search in Google Scholar
[[3] Cao, X., Wei, Y., Wen, F. and Sun, J. 2014, ‘Face alignment by explicit shape regression’, International Journal of Computer Vision 107(2), 177–190.]Search in Google Scholar
[[4] Cortes, C. and Vapnik, V., 1995. Support-vector networks. Machine learning, 20(3), pp.273-297.10.1007/BF00994018]Search in Google Scholar
[[5] Dalal, N. and Triggs, B., 2005, June. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE.]Search in Google Scholar
[[6] Karpathy, A. and Fei-Fei, L. 2015, Deep visual-semantic alignments for generating image descriptions, in ‘Proceedings of the IEEE conference on computer vision and pattern recognition’, pp. 3128–3137.]Search in Google Scholar
[[7] King, D.E., 2009. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research, 10(Jul), pp.1755-1758.]Search in Google Scholar
[[8] Kingma, D. P. and Ba, J. 2014, ‘Adam: A method for stochastic optimization’, arXiv preprint arXiv:1412.6980.]Search in Google Scholar
[[9] Kowalski, M., Naruniec, J. and Trzcinski, T. 2017, ‘Deep alignment network: A convolutional neural network for robust face alignment’, CoRR abs/1706.01789. URL: http://arxiv.org/abs1706.0178910.1109/CVPRW.2017.254]Search in Google Scholar
[[10] Kowalski, M. and Naruniec, J. 2016, ‘Face alignment using k-cluster regression forests with weighted splitting’, IEEE Signal Processing Letters 23(11), 1567–1571.]Search in Google Scholar
[[11] Lee, D., Park, H. and Yoo, C. D. 2015, Face alignment using cascade gaussian process regression trees, in ‘Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition’, pp. 4204–4212.]Search in Google Scholar
[[12] Le, V., Brandt, J., Lin, Z., Bourdev, L. and Huang, T. S. 2012, Interactive facial feature localization, in ‘European Conference on Computer Vision’, Springer, pp. 679–692.]Search in Google Scholar
[[13] Ren, S., Cao, X., Wei, Y. and Sun, J. 2014, Face alignment at 3000 fps via regressing local binary features, in ‘Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition’, pp. 1685–1692.]Search in Google Scholar
[[14] Sagonas, C., Tzimiropoulos, G., Zafeiriou, S. and Pantic, M. 2013, 300 faces in-the-wild challenge: The first facial landmark localization challenge, in ‘Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on’, IEEE, pp. 397–403.]Search in Google Scholar
[[15] Sebe, N. and Lew, M.S., 2013. Robust computer vision: Theory and applications (Vol. 26). Springer Science & Business Media.]Search in Google Scholar
[[16] Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. and Potts, C. 2013, Recursive deep models for semantic compositionality over a sentiment treebank, in ‘Proceedings of the 2013 conference on empirical methods in natural language processing’, pp. 1631–1642.]Search in Google Scholar
[[17] Trigeorgis, G., Snape, P., Nicolaou, M. A., Antonakos, E. and Zafeiriou, S. 2016, Mnemonic descent method: A recurrent process applied for end-to-end face alignment, in ‘Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition’, pp. 4177–4187.]Search in Google Scholar
[[18] Wen, Z. and Huang, T.S., 2006. 3D Face Processing: Modeling, Analysis and Synthesis (Vol. 8). Springer Science & Business Media.]Search in Google Scholar
[[19] Xiao, S., Feng, J., Xing, J., Lai, H., Yan, S. and Kassim, A. 2016, Robust facial landmark detection via recurrent attentive-refinement networks, in ‘European conference on computer vision’, Springer, pp. 57–72.]Search in Google Scholar
[[20] Xiong, X. and De la Torre, F. 2013, Supervised descent method and its applications to face alignment, in ‘Proceedings of the IEEE conference on computer vision and pattern recognition’, pp. 532–539.]Search in Google Scholar
[[21] Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R. and Toderici, G. 2015, Beyond short snippets: Deep networks for video classification, in ‘Proceedings of the IEEE conference on computer vision and pattern recognition’, pp. 4694–4702.]Search in Google Scholar
[[22] Yuksel, K., Chang, X. and Skarbek, W., 2017, August. Smile detectors correlation. In Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017 (Vol. 10445, p. 104451L). International Society for Optics and Photonics.10.1117/12.2280760]Search in Google Scholar
[[23] Zhu, S., Li, C., Change Loy, C. and Tang, X. 2015, Face alignment by coarse-to-fine shape searching, in ‘Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition’, pp. 4998–5006.]Search in Google Scholar
[[24] Zhu, X. and Ramanan, D., 2012, June. Face detection, pose estimation, and landmark localization in the wild. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 2879-2886). IEEE.]Search in Google Scholar
