Task-aware image quality estimators for face detection

Understanding the quality of face images plays a critical role in enhancing the efficiency of end-to-end face analytics systems, which perform tasks such as face detection, alignment, and recognition in a sequential manner. Recently, the development of face quality estimators (QEs) specifically for face recognition has received significant attention. However, in end-to-end face analytics systems, the performance of the face detector directly affects the face recognizer. Thus, assessing the suitability of an image for face detection before passing it on to face recognition can improve the resource utilization in such systems. In this research, we first introduce the detectability (DET) score of an image, a novel quality metric that links image quality to face detection performance. We use this DET score to design two novel QEs for face detection: supervised face detection quality estimator (SFDQE) and unsupervised face detection quality estimator (UFDQE). We also propose the mAP vs. reject protocol (mvR), a systematic evaluation protocol for assessing QEs in the context of face detection. In our experiments, we illustrate the effectiveness of SFDQE and UFDQE in determining the suitability of an image for face detection. Furthermore, we show the ability of our QEs to generalize; each is a powerful tool for image quality estimation in general object detection scenarios.