access icon free Design and evaluation of photometric image quality measures for effective face recognition

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.

Inspec keywords: image matching; design engineering; face recognition

Other keywords: FQI; face image quality index; automated face recognition system; sharpness; contrast; brightness; face recognition performance; matching performance; image quality factors; focus; Z-tests; photometric image quality measures; illumination; multiple quality measures

Subjects: Computer vision and image processing techniques; Image recognition; Project and design engineering

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