The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Computer Vision by Cooperative Focus and Stereo
Active Computer Vision by Cooperative Focus and Stereo
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Meta-Analysis of Face Recognition Algorithms
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Performance Modeling and Prediction of Face Recognition Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Factors that influence algorithm performance in the Face Recognition Grand Challenge
Computer Vision and Image Understanding
FRVT 2006 and ICE 2006 Large-Scale Experimental Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Incorporating image quality in multi-algorithm fingerprint verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A Comparative Study of Fingerprint Image-Quality Estimation Methods
IEEE Transactions on Information Forensics and Security
Fusing Face-Verification Algorithms and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to face recognition and evaluation of algorithm performance
Computational Statistics & Data Analysis
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A study is presented showing how three state-of-the-art algorithms from the Face Recognition Vendor Test 2006 (FRVT 2006) are effected by factors related to face images and people. The recognition scenario compares highly controlled images to images taken of people as they stand before a camera in settings such as hallways and outdoors in front of buildings. A Generalized Linear Mixed Model (GLMM) is used to estimate the probability an algorithm successfully verifies a person conditioned upon the factors included in the study. The factors associated with people are: Gender, Race, Age and whether they wear Glasses. The factors associated with images are: the size of the face, edge density and region density. The setting, indoors versus outdoors, is also a factor. Edge density can change the estimated probability of verification dramatically, for example from about 0.15 to 0.85. However, this effect is not consistent across algorithm or setting. This finding shows that simple measurable factors are capable of characterizing face quality; however, these factors typically interact with both algorithm and setting.