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Facial Recognition System Screening Evaluation Methodology for Complexion Biases

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2011 ASEE Annual Conference & Exposition


Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011



Conference Session

New Research & Trends for Minorities in Engineering

Tagged Division

Minorities in Engineering

Page Count


Page Numbers

22.697.1 - 22.697.12



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Paper Authors


Rigoberto Chinchilla Eastern Illinois University

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Dr. Rigoberto Chinchilla (Ph.D. in Integrated Engineering, Ohio University) is an Associate Professor in the School of Technology since 2004 and Current Interim Coordinator of Graduate Studies for the School of Technology at Eastern Illinois University. His teaching and research interests include Applied Statistics, Quality Assurance, Computer and Biometric Security, Information Systems, and Automation. Dr. Chinchilla has been a Fulbright scholar, a recipient of a United Nations scholarship, chosen as a Faculty Marshall for the Graduate School, and received an Achievement and Contribution Award as well as the "Excellence in the Use of Technology " (research) at EIU. His publications include: "Ethical and Social Consequences of Biometric Technologies in the USA," "Technology in Central America and the Impact on CAFTA," and "Design of an Industrial Control Laboratory," amongst others. Dr. Chinchilla has been awarded numerous grants and serves in numerous departmental and university committees at Eastern Illinois University.

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Harold Jay Harris Eastern Illinois University School of Technology

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Facial Recognition System Screening Evaluation Methodology for Complexion Biases Over the years, facial recognition systems (FRS) have come under scrutiny fromwatchdog groups who voice their complaints concerning the potential existence of FRSbiases towards certain cultures of people when deployed in security screening situations.Psychology has used the term “other-race effect” to describe this occurrence in humans.Researchers of FRS have used the theory behind the other-race effect to describe theoccurrence of bias associated with algorithms tested during the Face Recognition VendorTest of 2006. In this paper, we develop a scientific methodology based on the factors ofillumination, distance, and angle to evaluate whether or not an operating biometricsystem exhibits a significant bias towards a light complexion 3D facial model over a darkcomplexion 3D facial model. The paper also explains how to integrate this methodologywithin an educational curriculum either in DOE (Design of Experiments) or Biometricscourses. Commercial face recognition (SDK) software has been selected to test themethodology. Using classical DOE techniques, ANOVA and multiple regressionanalysis, this research shows step by step how to assess if biases are present in a facerecognition biometric system toward two different skin complexions. The methodologycan be equally used to test face recognition systems biases toward other racecharacteristics different than the color of the skin. This methodology can easily beextended to include more factors related with the biometric software system in theassessment. A detailed laboratory guide for the students has been developed in theprocess.Index Terms – Biometrics, Facial Recognition, Other-race Effect, Ethics, Minorities

Chinchilla, R., & Harris, H. J. (2011, June), Facial Recognition System Screening Evaluation Methodology for Complexion Biases Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--17978

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