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Title: Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults

Abstract

In this work, we examine if current state-of-the-art deep- learning enhanced face recognition systems exhibit a negative bias for children as compared to adults. The systems selected for this work are five top performing1 commercial off-the-shelf (COTS) face recognition systems, two government off-the-shelf (GOTS) face recognition systems and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on over the last few years. Our findings show a negative bias (i.e., a considerable degradation in performance) for each algorithm on children when compared to the performance obtained on adults. Genuine and imposter distributions high- light the performance bias between the datasets further sup- porting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score- level fusion strategies were evaluated. This work identifies the best score-level fusion technique for child demographic.

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [1];  [4]
  1. ORNL
  2. University of North Carolina, Wilmington
  3. Defence Science and Technology Group
  4. Florida Institute of Technology
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1559665
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019) - Long Beach, CA, California, United States of America - 6/16/2019 8:00:00 PM-6/20/2019 8:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Srinivas, Nisha, Ricanek, Karl, Michalski, Dana, Bolme, David, and King, Michael. Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults. United States: N. p., 2019. Web.
Srinivas, Nisha, Ricanek, Karl, Michalski, Dana, Bolme, David, & King, Michael. Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults. United States.
Srinivas, Nisha, Ricanek, Karl, Michalski, Dana, Bolme, David, and King, Michael. 2019. "Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults". United States. https://www.osti.gov/servlets/purl/1559665.
@article{osti_1559665,
title = {Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults},
author = {Srinivas, Nisha and Ricanek, Karl and Michalski, Dana and Bolme, David and King, Michael},
abstractNote = {In this work, we examine if current state-of-the-art deep- learning enhanced face recognition systems exhibit a negative bias for children as compared to adults. The systems selected for this work are five top performing1 commercial off-the-shelf (COTS) face recognition systems, two government off-the-shelf (GOTS) face recognition systems and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on over the last few years. Our findings show a negative bias (i.e., a considerable degradation in performance) for each algorithm on children when compared to the performance obtained on adults. Genuine and imposter distributions high- light the performance bias between the datasets further sup- porting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score- level fusion strategies were evaluated. This work identifies the best score-level fusion technique for child demographic.},
doi = {},
url = {https://www.osti.gov/biblio/1559665}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
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