2DLDA face recognition algorithm using novel texture features

Since the existing texture features-based face recognition methods are suffered from large texture feature dimensions and noises, two novel complementary texture features, named center quad binary pattern (C-QBP) and simplified quad binary pattern (S-QBP), are proposed. Based on the proposed C-QBP and S-QBP, the two dimensional linear discriminant analysis (2DLDA) subspace learning algorithm is further employed to realize face recognition. More specifically, a multi-level block division method is firstly performed on the input image to produce multiple image blocks. Then, the C-QBP and S-QBP feature histograms are extracted from each image block for establishing the texture matrix of the input image. Finally, the 2DLDA subspace learning algorithm is applied to find an optimal texture subspace for face recognition. Experimental results have shown that the proposed face recognition method is superior to the state-of-the-art texture feature and subspace learning based face recognition methods. Specifically, when each training class holds 5 images, the face recognition rate of the proposed approach is 98.68% on the ORL database with a 48×4 feature dimension, 99.42% on the YALE database with a 48×36 feature dimension, and 91.73% on the FERET database with a 48×26 feature dimension.