9 May 2018 Factor analysis in automated face detection: gender, occlusion, eyewear, brightness, contrast, and focus measure
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Abstract
Computer-based facial recognition algorithms exploit the unique characteristics of faces in images. However, in non-cooperative situations these unique characteristics are often disturbed. In this study, we examine the effect of six different factors on face detection in an unconstrained imaging environment: image brightness, image contrast, focus measure, eyewear, gender, and occlusion. The aim of this study is twofold: first, to quantify detection rates of conventional Haar cascade algorithms across these six factors; and second, to propose methods for automatically labeling datasets whose size prohibits manual labeling. First, we manually classify a uniquely challenging dataset comprising 9,688 images of passengers in vehicles acquired from a roadside camera system. Next, we quantify how each of the aforementioned factors affect face detection on this dataset. Of the six factors studied, occlusion had the most significant impact, resulting in a 54% decrease in detection rate between unoccluded and severely occluded faces in our unique dataset. Finally, we provide a methodology for data analytics of large datasets where manual labeling of the whole dataset is not possible.
Conference Presentation
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Benjamin Jafek, Benjamin Jafek, Matthew Eicholtz, Matthew Eicholtz, John Hendershott, John Hendershott, Christi Johnson, Christi Johnson, David Bolme, David Bolme, Hector Santos-Villalobos, Hector Santos-Villalobos, } "Factor analysis in automated face detection: gender, occlusion, eyewear, brightness, contrast, and focus measure", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065203 (9 May 2018); doi: 10.1117/12.2311281; https://doi.org/10.1117/12.2311281
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