A Bayesian Approach to Face Hallucination Using DLPP and KRR

Low resolution faces are the main barrier to efficient face recognition and identification in several problems primarily surveillance systems. To mitigate this problem we proposes a novel learning based two-step approach by the use of Direct Locality Preserving Projections (DLPP), Maximum a posterior estimation (MAP) and Kernel Ridge Regression (KRR) for super-resolution of face images or in other words Face Hallucination. First using DLPP for manifold learning and MAP estimation, a smooth Global high resolution image is obtained. In second step to introduce high frequency components KRR is used to model the Residue high resolution image, which is then added to Global image to get final high quality detail featured Hallucinated face image. As shown in experimental results the proposed system is robust and efficient in synthesizing low resolution faces similar to the original high resolution faces.