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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.<\/p>","DOI":"10.4018\/ijcini.2017100102","type":"journal-article","created":{"date-parts":[[2017,12,4]],"date-time":"2017-12-04T11:10:33Z","timestamp":1512385833000},"page":"22-40","source":"Crossref","is-referenced-by-count":2,"title":["Regression-Based Automated Facial Image Quality Model"],"prefix":"10.4018","volume":"11","author":[{"given":"Fatema Tuz","family":"Zohra","sequence":"first","affiliation":[{"name":"University of Calgary, Calgary, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei D.","family":"Gavrilov","sequence":"additional","affiliation":[{"name":"University of British Columbia, Calgary, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar A.","family":"Zatarain","sequence":"additional","affiliation":[{"name":"International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Laboratory for 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