NDLI: Design and evaluation of photometric image quality measures for effective face recognition

Content Provider	IET Digital Library
Author	Abaza, Ayman Harrison, Mary Ann Bourlai, Thirimachos Ross, Arun
Abstract	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.
Starting Page	314
Ending Page	324
Page Count	11
ISSN	20474938
Volume Number	3
e-ISSN	20474946
Issue Number	Issue 4, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/3/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2014.0022
Journal	IET Biometrics
Publisher Date	2014-07-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Face Recognition System Brightness Computer Vision And Image Processing Technique Contrast Design Engineering Face Image Quality Index Face Recognition Face Recognition Performance Focus FQI Illumination Image Matching Image Quality Factors Image Recognition Matching Performance Multiple Quality Measures Photometric Image Quality Measures Project And Design Engineering Sharpness Z-tests
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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