Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image Representation for Face Recognition

Abstract

Local Binary Patterns (LBP) is one of the efficient approaches for image representation, especially in the face recognition field. The motivation of the present study is to find a compact descriptor which captures texture information and yet is robust against several visual challenges such as illumination variation, facial expressions and head pose variation. The proposed approach, called it Enhance Line Local Binary Patterns (EL-LBP), is an improvement of 1D-Local Binary Patterns (1D-LBP) by reducing the dimension of feature vectors within 1D-LBP histogram and it leads to decrease the time cost during the matching stage. Experiments using ORL, Yale and AR datasets show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.