Face Recognition Fusing Multichannel Information

To extract more information from face images and promote the recognition efficiency,a novel 2D face recognition approach is proposed for fusing multichannel information of face image.First,a face image is decomposed into four subimages by the Haar wavelets transform.Then,each subimage is represented by a new face representation approach,Haar local binary pattern histogram (HLBPH).Finally,two novel match models for face samples are presented to balance the different face regions.The linear weighted fusion strategy to fuse the four channels' facial information.Experimental results are tested on ORL and Yale face database,which show that the proposed algorithm is effective and robust to the facial pose,expression,and illumination conditions.