Estimating Image Quality for Person Re-Identification
Haoyu Chen, Edward J. Delp, Amy R. Reibman · 2021
Task-based image quality is an important component in designing a real-life analytics system, and has been studied in many fields such as biometric recognition and pedestrian detection. Person re-identification (re-id) is an application in surveillance where one attempts to identify a person after they leave the surveillance system and then re-enter it. As a newer field in recognition applications, re-id lacks image quality-related research. In this paper, we propose an unsupervised and automated quality measure for the query images used in re-id, which we call "identifiability". Images that are less identifiable are more challenging for any re-identification system, creating output results that would likely be less reliable. Our proposed method measures feature consistency in the presence of perturbations as an indicator of identifiability. We then introduce two evaluation protocols for such a quality measure. We demonstrate our proposed quality measure is effective at ranking an image’s usefulness to a recognition system, and at identifying an image’s robustness against further compression.