Incremental Cascaded Continuous Regression
IMPORTANT: USAGE IS AS FOLLOWS
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Download and unrar the model, from: https://uniofnottm-my.sharepoint.com/:u:/g/personal/enrique_sanchezlozano_nottingham_ac_uk/EZJmYC5b2IJErTYFIR3IqKcBhdyURabHTpv-KINsjBny_w?e=N5vDBq
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In Matlab, load model.mat
2.1) model.mat contains the following variables: "model" and "params".
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Define video = 'path_to_video.format' to process an existing video, or video = [] to process a webcam stream
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Call track(model, video, params);
Example:
load model.mat
video = [];
data = track(model, video, params);
data will store the tracked points for the processed video
Copyright © 2018. Enrique Sánchez-Lozano
This is the Matlab code for paper:
[1] Enrique Sánchez-Lozano, Georgios Tzimiropoulos, Brais Martinez, Fernando De la Torre and Michel Valstar. A Functional Regression approach to Facial Landmark Tracking. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2018 (Early access). https://ieeexplore.ieee.org/abstract/document/8017515/
[2] Enrique Sánchez-Lozano, Brais Martinez, Georgios Tzimiropoulos and Michel Valstar. Cascaded Continuous Regression for Real-time Incremental Face Tracking. In: ECCV (VIII) pp. 645—661. 2016.
Important: Code is released as is for research purposes only.
CCR models have been trained using the LFPW, HELEN, AFW, IBUG, Multi-PIE and 300VW (training partition) datasets.
Should you use the code as is, please cite [1] and [2]. Should you use your own initialisation system, please cite [1]. The (bounding box) face detection utilises the DLIB library, and has been compiled only in Windows, and therefore is not yet available for Mac or Linux. Instead, the built-in Viola-Jones-based face detection system is used, which may result in a drop of performance.
Contact: Enrique Sánchez-Lozano, Enrique.SanchezLozano@nottingham.ac.uk