Fusion with Layered Features of LBP and HOG for Face Recognition

Local binary pattern(LBP) has limitation in extracting texture feature and cannot effectively depict the edge and direction information,thus a new method is proposed,called layered fusion with LBP and histogram of oriented gradients(HOG) features.First,LBP operator is adopted to extract the layered texture spectrum feature of an image,and then the edge features of the original image are extracted by using HOG operator,as well as the layered HOG features which are based on the layered LBP.Finally,the layered LBP features with these two different HOG edge features are fused to generate two different layered fusion features.The experiments are implemented on ORL,Yale,GT face databases by comparing fifteen algorithms,which show that the layered fusion features generated by the fusion method of this paper perform much better than the traditional dimension-reduced algorithms,single LBP and single HOG.The corresponding recognition rates of the proposed method are significantly improved,of which the best are 99%,99.5%and 99.14%,respectively.