Evaluation of super-resolution methods for improving face landmark localisation performance

Super-resolution methods have proven to be useful in the face domain, where they can be used to improve the quality of low-resolution images, as well as images subjected to different forms of visual degradation, such as out-of-focus, shot noise and motion blur. Super-resolution methods can be used on such data to improve the performance of various biometric systems, such as face recognition and surveillance systems. Landmark localisation is of key importance in biometric systems, since it is typically used in the image processing pipeline to align face images to a canonical pose expected by automatic face recognition systems. This paper evaluates the use of face superresolution systems in terms of their effect on the performance of face landmark localisation methods. Experiments with different face super-resolution systems show that the impact of super-resolution on face landmark localisation is heavily dependent on the specific super-resolution method, however, the impact of each tested method is positive in comparison to landmark localisation directly from low-resolution face images. Based on our findings, the inclusion of face superresolution as one of the first pre-processing steps in the face recognition pipeline is recommended.