Paper Info
Title
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/.
Year
DOI
Venue
2019
10.1016/j.image.2018.12.013
Signal Processing: Image Communication
Keywords
Field
DocType
Face Quality Assessment,Face recognition,Super-resolution,Fiducial point estimation,Face image database
Point estimation,Facial recognition system,Computer vision,Fiducial marker,Computer science,Image processing,Unavailability,Artificial intelligence,Biometrics,Database
Journal
Volume
ISSN
Citations 
74
0923-5965
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Shubhobrata Bhattacharya121.71
Suparna Rooj211.70
Aurobinda Routray333752.80