Factors that influence algorithm performance in the Face Recognition Grand Challenge

A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of verification to subject and image covariates...

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Published inComputer vision and image understanding Vol. 113; no. 6; pp. 750 - 762
Main Authors Beveridge, J. Ross, Givens, Geof H., Phillips, P. Jonathon, Draper, Bruce A.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Inc 01.06.2009
Elsevier
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Face recognition
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Performance analysis
Software
Statistical modeling
Subject covariates
Statistical modeling
Subject covariates
Performance analysis
Face recognition
Performance evaluation
Computer vision
Generalized linear model
Image resolution
Statistical analysis
Probabilistic approach
Image processing
Sex
Pattern recognition
Modeling
Mixed model
Facies
Age
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Abstract A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of verification to subject and image covariates. The data and algorithms are selected from the Face Recognition Grand Challenge and the results show that the effects of covariates are strong and algorithm specific. The paper presents in detail all of the significant effects including interactions among covariates. One significant conclusion is that covariates matter. The variation in verification rates as a function of covariates is greater than the difference in average performance between the two best algorithms. Another is that few or no universal effects emerge; almost no covariates effect all algorithms in the same way and to the same degree. To highlight one specific effect, there is evidence that verification systems should enroll subjects with smiling rather than neutral expressions for best performance.
AbstractList A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of verification to subject and image covariates. The data and algorithms are selected from the Face Recognition Grand Challenge and the results show that the effects of covariates are strong and algorithm specific. The paper presents in detail all of the significant effects including interactions among covariates. One significant conclusion is that covariates matter. The variation in verification rates as a function of covariates is greater than the difference in average performance between the two best algorithms. Another is that few or no universal effects emerge; almost no covariates effect all algorithms in the same way and to the same degree. To highlight one specific effect, there is evidence that verification systems should enroll subjects with smiling rather than neutral expressions for best performance.
A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of verification to subject and image covariates. The data and algorithms are selected from the Face Recognition Grand Challenge and the results show that the effects of covariates are strong and algorithm specific. The paper presents in detail all of the significant effects including interactions among covariates. One significant conclusion is that covariates matter. The variation in verification rates as a function of covariates is greater than the difference in average performance between the two best algorithms. Another is that few or no universal effects emerge; almost no covariates effect all algorithms in the same way and to the same degree. To highlight one specific effect, there is evidence that verification systems should enroll subjects with smiling rather than neutral expressions for best performance.
Author Givens, Geof H.
Phillips, P. Jonathon
Beveridge, J. Ross
Draper, Bruce A.
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Issue 6
Keywords Statistical modeling
Subject covariates
Performance analysis
Face recognition
Performance evaluation
Computer vision
Generalized linear model
Image resolution
Statistical analysis
Probabilistic approach
Image processing
Sex
Pattern recognition
Modeling
Mixed model
Facies
Age
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Snippet A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition...
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SubjectTerms Applied sciences
Artificial intelligence
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Face recognition
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Performance analysis
Software
Statistical modeling
Subject covariates
Title Factors that influence algorithm performance in the Face Recognition Grand Challenge
URI https://dx.doi.org/10.1016/j.cviu.2008.12.007
https://www.proquest.com/docview/34404379
https://www.proquest.com/docview/903628604
Volume 113
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