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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationDisruptive Technologies in Information Sciences
EditorsMisty Blowers, Russell D. Hall, Venkateswara R. Dasari
PublisherSPIE
ISBN (Electronic)9781510618152
DOIs
StatePublished - 2018
EventDisruptive Technologies in Information Sciences 2018 - Orlando, United States
Duration: Apr 17 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10652
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDisruptive Technologies in Information Sciences 2018
Country/TerritoryUnited States
CityOrlando
Period04/17/1804/18/18

Funding

1This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • face recognition
  • image processing
  • machine learning
  • object detection

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