3D Face Pose Tracking using Low Quality Depth Cameras

By Ahmed Rekik, Achraf Ben-Hamadou, Walid Mahdi. 2013

18 citations recorded by the source corpus.

This paper presents a new method for 3D face pose tracking in color image and depth data acquired by RGB-D (i.e., color and depth) cameras (e.g., Microsoft Kinect, Canesta, etc.). The method is based on a particle filter formalism. Its main contribution lies in the combination of depth and image data to face the poor signal-tonoise ratio of low quality RGB-D cameras. Moreover, we consider a visibility constraint to handle partial occlusions of the face. We demonstrate the accuracy and the robustness of our method by performing a set of experiments on the Biwi Kinect head pose database.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Li Liu
2. Jungong Han
3. Ling Shao
4. Ziyun Cai
5. Yi-Zhe Song
6. Jianbing Shen
7. Wenguan Wang
8. Tao A. Xiang

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “Some algorithms tested on this dataset can be found in [4, 56, 71].”