Human Ear Recognition Using HOG with PCA Dimension Reduction and LBP

Human ear recognition has attracted more attention in the past several years. In terms of the redundant information of Histogram of Oriented Gradient (HOG), this paper proposes a human ear recognition method in which the principal component analysis (PCA) was carried out on the HOG dimension reduction, then fused with local binary pattern (LBP) features. Using minimum distance classification for classification, experiments on USTB human ear image library shows that the proposed method improves the recognition rate and speeds up the training and detection.