ML Anthology

3D Spatio-Temporal Face Recognition Using Dynamic Range Model Sequences

Yi Sun, Lijun Yin
CVPRW 2008 pp. 1-7
doi:10.1109/CVPRW.2008.4563125 /cvprw/2008/sun2008cvprw-3d/

Abstract

Research on 3D face recognition has been intensified in recent years. However, most research has focused on the 3D static data analysis. In this paper, we investigate the face recognition problem using dynamic 3D face model sequences. Based on our newly created 3D dynamic face database, we propose to use a spatio-temporal hidden Markov model (HMM) which incorporates 3D surface feature characterization to learn the spatial and temporal information of faces. The advantage of using 3D dynamic data for face recognition has been evaluated by comparing our approach to three conventional approaches: 2D video based temporal HMM model, conventional 2D-texture based approach (e.g., Gabor wavelet based approach), and static 3D-model-based approaches.

CVPRW Semantic Scholar

Cite

Text

Sun and Yin. "3D Spatio-Temporal Face Recognition Using Dynamic Range Model Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008. doi:10.1109/CVPRW.2008.4563125

Markdown

[Sun and Yin. "3D Spatio-Temporal Face Recognition Using Dynamic Range Model Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008.](https://mlanthology.org/cvprw/2008/sun2008cvprw-3d/) doi:10.1109/CVPRW.2008.4563125

BibTeX

@inproceedings{sun2008cvprw-3d,
  title     = {{3D Spatio-Temporal Face Recognition Using Dynamic Range Model Sequences}},
  author    = {Sun, Yi and Yin, Lijun},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2008},
  pages     = {1-7},
  doi       = {10.1109/CVPRW.2008.4563125},
  url       = {https://mlanthology.org/cvprw/2008/sun2008cvprw-3d/}
}