A Comprehensive Survey on Face Quality Detection in a Video Frame

Abstract

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms.