Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Statistical Models in S
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Statistical Performance Evaluation of Biometric Authentication Systems Using Random Effects Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Factors that influence algorithm performance in the Face Recognition Grand Challenge
Computer Vision and Image Understanding
A meta-analysis of face recognition covariates
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
An introduction to biometric-completeness: the equivalence of matching and quality
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
FRVT 2006: Quo Vadis face quality
Image and Vision Computing
Are younger people more difficult to identify or just a peer-to-peer effect
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
An other-race effect for face recognition algorithms
ACM Transactions on Applied Perception (TAP)
A novel statistical model to evaluate the performance of EBGM based face recognition
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Introduction to face recognition and evaluation of algorithm performance
Computational Statistics & Data Analysis
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Recognition difficulty is statistically linked to 11 subject covariate factors such as age and gender for three face recognition algorithms: principle components analysis, an interpersonal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender, age, glasses use, facial hair, bangs, mouth state, complexion, state of eyes, makeup use, and facial expression. We use two statistical models. First, an ANOVA relates covariates to normalized similarity scores. Second, logistic regression relates subject covariates to probability of rank one recognition. These models have strong explanatory power as measured by R2 and deviance reduction, while providing complementary and corroborative results. Some factors, like changes to the eye status, affect all algorithms similarly. Other factors, such as race, affect different algorithms differently. Tabular and graphical summaries of results provide a wealth of empirical evidence. Plausible explanations of many results can be motivated from knowledge of the algorithms. Other results are surprising and suggest a need for further study