Tiny Faces, Big Trouble: Evaluating Super-Resolution for Face Recognition
Xavier Merino, Gabriella Pangelinan, Samuel Langborgh, Michael C. King · 2025
Low-resolution imagery presents a critical challenge for face recognition (FR), particularly in use cases such as law enforcement and surveillance, where real-world conditions are unconstrained. Despite the development of FR systems tailored for low-resolution input and the availability of super-resolution (SR) techniques, there is no evidence that such enhancements are used in operational deployments. This work evaluates the effectiveness of six SR methods in enhancing low-resolution face images prior to recognition. We simulate low-resolution probes at interpupillary distances (IPD) of 5-30px and upscale them using SR methods, while keeping gallery images fixed at high resolution (~100px IPD). Our analysis proceeds in two stages. First, we assess whether SR methods preserve image fidelity using standard image quality assessment (IQA) metrics and 1:1 “self-matching” scores. Second, we measure their impact on biometric performance by performing 1:1 and 1:N matching. Results show that although SR techniques improve perceptual quality, they do not fully recover identity-relevant features, especially at lower resolutions. These findings highlight the limitations of current SR methods in restoring biometric utility and underscore the need for resolution-aware FR pipelines in real-world applications.