Local Gabor Binary Pattern Random Subspace Method for eyeglasses-face recognition

In this paper, we introduce a novel Local Gabor Binary Pattern Random Subspace Method (LGBPRSM) for wearing-glasses face recognition. It extracts the discriminating features from facial space based on local-feature method, after that, it constructs multiple classifiers by randomly sampling from the feature set to gain more diversity between classifiers for efficiently recognizing the faces with glasses. Our experimental results on FERET and Yale database prove the advantages of the proposed approach when compared with other methods.