
[1] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multi-task cascaded convolutional networks,” arXiv preprint arXiv:1604.02878, 2016.
[2] H. Fan and E. Zhou, “Approaching human level facial landmark localization by deep learning,” Image and Vision Computing, vol. 47, 2016, pp. 27–35.
[3] Z. Zhang, P. Luo, C. C. Loy, and X. Tang, “Facial landmark detection by deep multi-task learning,” in European Conference on Computer Vision. Springer, 2014, pp. 94–108.
[4] C. Ding, J. Choi, D. Tao, and L. S. Davis, “Multi-directional multi-level dual-cross patterns for robust face recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 3, 2016, pp. 518–531.
[5] Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3476–3483.
[6] J. Cˇ ech, V. Franc, M. Urˇicˇa´rˇ, and J. Matas, “Multi-view facial landmark detection by using a 3d shape model,” Image and Vision Computing, vol. 47, 2016, pp. 60–70.
[7] M. Uˇriˇc´aˇr, V. Franc, D. Thomas, A. Sugimoto, and V. Hlav´aˇc, “Multi-view facial landmark detector learned by the structured
output svm,” Image and Vision Computing, vol. 47, 2016, pp. 45–59.
[8] V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1867–1874.
[9] S. Cheng, A. Asthana, S. Zafeiriou, J. Shen, and M. Pantic, “Real-time generic face tracking in the wild with cuda,” in Proceedings of the 5th ACM Multimedia Systems Conference. ACM, 2014, pp. 148–151.
[10] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint arXiv:1408.5093, 2014.
[11] “The massachusetts green high performance computing center.” [Online]. Available: http://www.mghpcc.org/
[12] P. Doll´ar, “Piotr’s Computer Vision Matlab Toolbox (PMT),” https://github.com/pdollar/toolbox.
[13] V. Jain and E. Learned-Miller, “Fddb: A benchmark for face detection in unconstrained settings,” University of Massachusetts, Amherst, Tech. Rep. UM-CS-2010-009, 2010.
[14] M. K¨ostinger, P. Wohlhart, P. M. Roth, and H. Bischof, “Annotated facial landmarks in the wild: A large-scale, realworld database for facial landmark localization,” in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on. IEEE, 2011, pp. 2144–2151.