Recently, the research in multispectral face recognition has focused on developing efficient frameworks for improving face recognition performance at close-up distances. However, few studies have investigated the multispectral face images captured at long distance. In fact, great challenges still exist in recognizing human face in images captured at long distance as the image quality might be affected and some important features masked. Therefore, multispectral face recognition tools and algorithms should evolve from close-up distances to long distances. To address these issues, we present in this paper a novel image fusion scheme based on Maximum Ratio Combining algorithm and improve multispectral face recognition at long distance. The proposed method is compared with similar super-resolution method based on the Maximum likelihood algorithm. Simulation results show the efficiency of the proposed approach in term of average variance of detection error.