Open AccessDesign and evaluation of photometric image quality measures for effective face recognition
Author(s) -
Abaza Ayman,
Harrison Mary Ann,
Bourlai Thirimachos,
Ross Arun
Publication year - 2014
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2014.0022
Subject(s) - computer science , image quality , facial recognition system , face (sociological concept) , artificial intelligence , quality (philosophy) , computer vision , image (mathematics) , pattern recognition (psychology) , information retrieval , social science , philosophy , epistemology , sociology
The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real‐time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the author's studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z‐tests that demonstrate the advantages of the proposed FQI in face recognition applications.