QDF: A face database with varying quality

Abstract

Face Recognition is one of the well-researched areas of biometrics. Although many researchers have shown considerable interest, the problems still persist because of unpredictable environmental factors affecting the acquisition of real-life face images. One of the major factors that causes poor recognition performance of the most face recognition algorithms is due to the unavailability of a proper training dataset which reflects real-life scenarios. In this paper, we propose a face dataset, of about 100 subjects, with varying degree of quality in terms of distance from the camera, ambient illumination, pose variations and natural occlusions. This database can be used to train systems with real-life face images. The face quality of this dataset has been quantified with popular Face Quality Assessment (FQA) algorithms. We have also tested this database with standard face recognition, super-resolution image processing and fiducial point estimation algorithms. Database is available to research community through https://sites.google.com/view/quality-based-distance-face-da/.