A repo for EECS 349 Machine Learning project for Northwestern University
In this project, we try to use AAM(Active Appearance Model) and ESR(Explicit Shape Regression)[1] to train the face alignment model and use this trained model to do the face alignment job.
We use the following dataset which are commonly used by face alignment research:
- Convert all the raw image into grayscale image
- Use the face detector provided by OpenCV to pre-compute the bouding box for alignment
- Extract the coordinates of landmark from the dataset
- Implement ESR algorithm to train an ESR model
- Use an existing AAM library to train an AAM model
- Optimize our ESR implementation by tuning and using better pixel selection strategy
- Study how the parameters influence the performance of ESR algorithm
- Compare the results of ESR and AAM
- Build up a demo using ESR on web page
- Complete the final report and present out results
Yang Yang, Haomin Hu, Can Wang, Lijun Tang
[1] Cao, Xudong, et al. "Face alignment by explicit shape regression." International Journal of Computer Vision 107.2 (2014): 177-190.
[2] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic. A semi-automatic methodology for facial landmark annotation. Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W’13), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG '13). Oregon, USA, June 2013.
[3] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge. Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W 2013), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia, December 2013.
[4] Burgos-Artizzu, Xavier P., Pietro Perona, and Piotr Dollár. "Robust face landmark estimation under occlusion." Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE, 2013.