This code is the implementation of our paper MCNet [1] for both 29 and 68 facial landmarks, using the matlab interface of Caffe [2]. We suggest you using caffe-happynear (https://github.com/happynear/caffe-windows), which is a popular compiled windows version of Caffe.
We give examples (mcnet_29.m, mcnet_68.m) for obtaining the face alignment results on COFW [3] and IBUG [4].
If you find our code useful in your research work, please cite [1].
Should you have any questions, don't hesitate to contact with us through email [email protected].
References:
[1] Zhiwen Shao, Hengliang Zhu, Yangyang Hao, Min Wang, and Lizhuang Ma, “Learning a multi-center convolutional network for unconstrained face alignment,” in IEEE International Conference on Multimedia and Expo. IEEE, 2017, pp. 109–114.
[2] Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B Girshick, Sergio Guadarrama, and Trevor Darrell, "Caffe: Convolutional architecture for fast feature embedding.," in ACM International Conference on Multimedia. ACM, 2014, pp. 675–678.
[3] Xavier P Burgos-Artizzu, Pietro Perona, and Piotr Dollar, "Robust face landmark estimation under occlusion," in IEEE International Conference on Computer Vision. IEEE, 2013, pp. 1513–1520.
[4] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic, "300 faces in-the-wild challenge: The first facial landmark localization challenge," in IEEE International Conference on Computer VisionWorkshops. IEEE, 2013, pp. 397–403.