alexa Face Verification Subject to Varying (Age, Ethnicity, and Gender) Demographics Using Deep Learning
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

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.



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.


Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals


[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

pharma[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version