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

Benjamin Jafek; Matthew Eicholtz; John Hendershott; Christi Johnson; David Bolme; Hector Santos-Villalobos

doi:10.1117/12.2311281

9 May 2018 Factor analysis in automated face detection: gender, occlusion, eyewear, brightness, contrast, and focus measure

Benjamin Jafek, Matthew Eicholtz, John Hendershott, Christi Johnson, David Bolme, Hector Santos-Villalobos

Author Affiliations +

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 1065203 (2018) https://doi.org/10.1117/12.2311281
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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

Citation Download Citation

Benjamin Jafek, Matthew Eicholtz, John Hendershott, Christi Johnson, David Bolme, and 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); https://doi.org/10.1117/12.2311281

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available