Face recognition using data driven local appearance features
Xin Xie, Chao Chen, Zhijian Chen
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.514How to use a DOI?
- Face recognition, local appearance features, data driven, feature extraction, point-to-subspace distance.
- A novel data driven face descriptor based on point-to-subspace metric is proposed for subspace classifiers. Unlike conventional feature descriptors which are carefully designed by hand, the newly proposed method uses supervised learning to derive more robust and more discriminative descriptors. During the feature extraction process, the point-to-subspace distance is used as the inner mechanism to train parameters including filters and weights of different pixels. Experimental results on FERET and Extended Yale B database show that when using subspace classifiers, the proposed feature descriptor is more discriminative and yields higher recognition rate over other features.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xin Xie AU - Chao Chen AU - Zhijian Chen PY - 2015/12 DA - 2015/12 TI - Face recognition using data driven local appearance features BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.514 DO - https://doi.org/10.2991/icmmcce-15.2015.514 ID - Xie2015/12 ER -