@inproceedings{f062a06f988c4ca292b6b20a601dd2a0,
title = "Essence of 2DPCA and modification method for face recognition",
abstract = "In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.",
keywords = "2DPCA, Face recognition, Principal component analysis (PCA)",
author = "Jun Hou and Gao, {Quan Xue} and Quan Pan and Zhang, {Hong Cai}",
year = "2006",
doi = "10.1109/ICMLC.2006.258473",
language = "英语",
isbn = "1424400619",
series = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",
pages = "3351--3353",
booktitle = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",
note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",
}