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Regression-Based Automated Facial Image Quality Model

Regression-Based Automated Facial Image Quality Model

Fatema Tuz Zohra, Andrei D. Gavrilov, Omar A. Zatarain, Marina L. Gavrilova
Copyright: © 2017 |Volume: 11 |Issue: 4 |Pages: 19
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522511724|DOI: 10.4018/IJCINI.2017100102
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MLA

Zohra, Fatema Tuz, et al. "Regression-Based Automated Facial Image Quality Model." IJCINI vol.11, no.4 2017: pp.22-40. http://doi.org/10.4018/IJCINI.2017100102

APA

Zohra, F. T., Gavrilov, A. D., Zatarain, O. A., & Gavrilova, M. L. (2017). Regression-Based Automated Facial Image Quality Model. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 11(4), 22-40. http://doi.org/10.4018/IJCINI.2017100102

Chicago

Zohra, Fatema Tuz, et al. "Regression-Based Automated Facial Image Quality Model," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 11, no.4: 22-40. http://doi.org/10.4018/IJCINI.2017100102

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Abstract

Nowadays, biometric technologies became reliable and widespread means of unobtrusive user authentication in a variety of real-world applications. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. The facial samples can be affected by various quality factors, such as uneven illumination, low or high contrast, excessive brightness, blurriness, etc. In this article, the authors propose a quality estimation method based on linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The weights assigned to the different quality factors by the linear regression model reflect the impact of those quality factors on the performance of the recognition system. Therefore, the prediction scores generated from the model is a strong indicator of the overall quality of the facial images. The authors evaluated the quality estimation model on the Extended Yale Database B. They also performed a study to understand which quality factors affect the face recognition the most.

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