alexa Face Verification Subject to Varying (Age, Ethnicity, a
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Face Verification Subject to Varying (Age, Ethnicity, and Gender) Demographics Using Deep Learning

Hachim El Khiyari* and Harry Wechsler

Department of Computer Science, George Mason University, Fairfax, VA 22032, USA

*Corresponding Author:
El Khiyari H
Department of Computer Science
George Mason University, Fairfax
VA 22030, USA
Tel: (703) 931-5206
Fax:(703) 993-1710
E-mail: [email protected]

Received Date: October 24, 2016; Accepted Date: November 26, 2016; Published Date: November 30, 2016

Citation: El Khiyari H, Wechsler H (2016) Face Verification Subject to Varying (Age, Ethnicity, and Gender) Demographics Using Deep Learning. J Biom Biostat 7:323. doi:10.4172/2155-6180.1000323

Copyright: © 2016 El Khiyari H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Human facial appearance is strongly influenced by demographical characteristics such as categorical age, ethnicity, and gender with each category further partitioned into classes-Black, White, Male, Female, Young (18-30), Middle Age (30-50), and Old (50-70)-and groups−mix of classes. Most subjects share a more similar appearance with their own demographic class than with other classes. We evaluate here the accuracy of automatic facial verification for subjects belonging to varying age, ethnicity, and gender categories. Towards that end, we use a convolutional neural network for feature extraction and show that our method yields better performance on individual demographics compared to a commercial face recognition engine. For one-class demographic groups, we corroborate empirical findings that biometric performance on verification is relatively lower for females, young subjects in the 18-30 age group, and blacks. We then expand the scope of our method and evaluate the accuracy of face verification for several multiclass demographic groups. We discuss the results and make suggestions for improving face verification across varying demographics.

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