@inproceedings{9dffebb775384b5f85d592bf5e0fc1e8,
title = "Facial pose estimation via dense and sparse representation",
abstract = "Facial pose estimation is an important part for facial analysis such as face and facial expression recognition. In most existing methods, facial features are essential for facial pose estimation. However, occluded key features and uncontrolled illumination of face images make the facial feature detection vulnerable. In this paper, we propose methods for facial pose estimation via dense reconstruction and sparse representation but avoid localizing facial features. The Sparse Representation Classifier (SRC) method has achieved successful results in face recognition. In this paper, we explore SRC in pose estimation. Sparse representation learns a dictionary of base functions, so each input pose can be approximated by a linear combination of just a sparse subset of the bases. The experiment conducted on the CMU Multiple face database has shown the effectiveness of the proposed method.",
keywords = "3D face, human face, linear regression, pose analysis",
author = "Hui Yu and Honghai Liu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Symposium on Robotic Intelligence in Informationally Structured Space, RiiSS 2014 ; Conference date: 09-12-2014 Through 12-12-2014",
year = "2015",
month = jan,
day = "13",
doi = "10.1109/RIISS.2014.7009177",
language = "英语",
series = "IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - RiiSS 2014: 2014 IEEE Symposium on Robotic Intelligence in Informationally Structured Space, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SSCI 2014",
address = "美国",
}