Matching facial images acquired in different electromagnetic spectral bands remains a challenge. An example of this type of comparison is matching active or passive infrared (IR) against a gallery of visible face images. When combined with cross-distance, this problem becomes even more challenging due to deteriorated quality of the IR data. As an example, we consider a scenario where visible light images are acquired at a short standoff distance while IR images are long range data. To address the difference in image quality due to atmospheric and camera effects, typical degrading factors observed in long range data, we propose two approaches that allow to coordinate image quality of visible and IR face images. The first approach involves Gaussian-based smoothing functions applied to images acquired at a short distance (visible light images in the case we analyze). The second approach involves denoising and enhancement applied to low quality IR face images. A quality measure tool called Adaptive Sharpness Measure is utilized as guidance for the quality parity process, which is an improvement of the famous Tenengrad method. For recognition algorithm, a composite operator combining Gabor filters, Local Binary Patterns (LBP), generalized LBP and Weber Local Descriptor (WLD) is used. The composite operator encodes both magnitude and phase responses of the Gabor filters. The combining of LBP and WLD utilizes both the orientation and intensity information of edges. Different IR bands, short-wave infrared (SWIR) and near-infrared (NIR), and different long standoff distances are considered. The experimental results show that in all cases the proposed technique of image quality parity (both approaches) benefits the final recognition performance.