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README.md

SDM: A Matlab implementation of Supervised Descent Method for facial landmark detection and tracking

Resources

  1. Feng, Z. H., Huber P., Kittler J., Christmas W. & Wu X. J. Random cascaded-regression copse for robust facial landmark detection. IEEE Signal Processing Letters, 2015, 1(22), pp:76-80. [ Link ]

  2. Feng, Z. H., Hu G., Kittler J., Christmas W. & Wu X. J. Cascaded collaborative regression for robust facial landmark detection trained using a mixture of synthetic and real images with dynamic weighting. IEEE Trans. on Image Processing, 2015, 24(11), pp:3425-3440. [ Link ]

  3. Xiong, X., & De la Torre, F. Supervised descent method and its applications to face alignment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp:532-539.

Guide for use

  1. Create a folder with name 'data' for storing training and test data, and a folder with name 'model' for storing a trained model, under the main directory

  2. Download the COFW color images from http://www.vision.caltech.edu/xpburgos/ICCV13/ and unzip the .mat files to the 'data' folder

  3. Run the example_detection.m code for SDM training and test for facial landmark detection

  • Notice: The code was tested on Matlab 2016a with the Computer Vision System Toolbox. If you do not have this tool box. You can use the vlfeat toolbox instead.

Contact

Dr. Zhenhua Feng

Centre for Vision, Speech and Signal Processing, University of Surrey

z.feng@surrey.ac.uk, fengzhenhua2010@gmail.com

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Matlab implementation of the Supervised Descent Method (SDM) for facial landmark detection and face tracking

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