Robust face recognition using trimmed linear regression
In this work, we focus on the problem of partially occluded face recognition. Using a robust estimator, we detect and trim the contaminated pixels from query sample. The corresponding pixels in the training samples are trimmed as well. The linear regression is applied to the trimmed images. Finally, the query image is labeled to the class with minimum normalized reconstruction error. Extensive experiments on benchmark face datasets demonstrate that the proposed approach is much more robust than state-of-the-art methods in dealing with occluded faces.