Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates. We use Natural Scene Statistics (NSS) to detect degradation of infrared images, and to adapt the face recognition algorithm to the quality of the test image. The proposed approach applied to a face identification algorithm based on thermal signatures yielded an improvement of rank one recognition rates between 11% and 19%. These results confirm the relevance of image quality assessment for improving biometric identification systems that use thermal images.