@inproceedings{9f16f66b3369411f885a4c53e113372b,
title = "Face detection using SVM trained in independent space",
abstract = "The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the Independent Component Analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.",
keywords = "Face detection, ICA, PCA, SVM",
author = "Gao, \{Quan Xue\} and Quan Pan and Zhang, \{Hong Cai\} and Cheng, \{Yong Mei\} and Tian, \{Qi Chuan\}",
year = "2004",
doi = "10.1109/ICMLC.2004.1380445",
language = "英语",
isbn = "0780384032",
series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3674--3677",
booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
note = "3rd International Conference on Machine Learning and Cybernetics, ICMLC 2004 ; Conference date: 26-08-2004 Through 29-08-2004",
}