Is Face Super Resolution Truly Pushing the Boundaries of Face Recognition?

With the improving efficacy of generative algorithms, the performance of face super-resolution algorithms is also increasing towards generating high-quality facial data. However, are these images useful for face recognition? This paper investigates whether these enhanced super-resolved facial images only improve the visual quality or they also aid in improving the recognizability of these images, thus contributing towards addressing the challenge of low-resolution face recognition. We conduct a comprehensive empirical and statistical analysis of human perception and face recognition tasks. Extensive experiments are performed using multiple state-of-the-art generative and face recognition models across six publicly available face datasets to assess whether face super-resolution algorithms are effective in recognizing individuals in low-resolution conditions. The results and supporting analysis indicate that the ability of super-resolution images to improve recognizability is limited, and further research is required to design generative AI algorithms that improve both visual appearance and recognizability of low-resolution images.