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Factor analysis in automated face detection: gender, occlusion, eyewear, brightness, contrast, and focus measure...

Publication Type
Conference Paper
Journal Name
Proceedings of SPIE
Publication Date
Page Number
Conference Name
SPIE Defense and Security
Conference Location
Orlando, Florida, United States of America
Conference Sponsor
Conference Date

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