2013 IEEE Conference on Computer Vision and Pattern Recognition (2005)
San Diego, California
June 20, 2005 to June 26, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2005.286
Random subspaces are a popular ensemble construction technique that improves the accuracy of weak classifiers. It has been shown, in different domains, that random subspaces combined with weak classifiers such as decision trees and nearest neighbor classifiers can provide an improvement in accuracy. In this paper, we apply the random subspace methodology to the 2-D face recognition task. The main goal of the paper is to see if the random subspace methodology can do as well, if not better, than the single classifier constructed on the tuned face space. We also propose the use of a validation set for tuning the face space, to avoid bias in the accuracy estimation. In addition, we also compare the random subspace methodology to an ensemble of subsamples of image data. This work shows that a random subspaces ensemble can outperform a well-tuned single classifier for a typical 2-D face recognition problem. The random subspaces approach has the added advantage of requiring less careful tweaking.
Face recognition, Nearest neighbor searches, Principal component analysis, Filtering, Testing, Classification tree analysis, Computer science, Decision trees, Pixel, Pattern recognition
"Random subspaces and subsampling for 2-D face recognition", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 02, no. , pp. 582,583,584,585,586,587,588,589, 2005, doi:10.1109/CVPR.2005.286