Face detection using SVM trained in independent space
Quan-Xue Gao
Quan Pan
Hong-Cai Zhang
Yong-Mei Cheng
Qi-Chuan Tian
Dept. of Autom. Control, Northwestern Polytech Univ., Xi'an, China;
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.
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