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
Strategies and Benefits of Fusion of 2D and 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel correlation filter based redundant class-dependence feature analysis (KCFA) on FRGC2.0 data
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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Recent work indicates that state-of-the-art face recognition algorithms can surpass humans matching identity in pairs of face images taken under different illumination conditions. It has been demonstrated further that fusing algorithm- and human-derived face similarity estimates cuts error rates substantially over the performance of the best algorithms. Here we employed a pattern-based classification procedure to fuse individual human subjects and algorithms with the goal of determining whether strategy differences among humans are strong enough to suggest particular man-machine combinations. The results showed that error rates for the pairwise man-machine fusions were reduced an average of 47 percent when compared to the performance of the algorithms individually. The performance of the best pairwise combinations of individual humans and algorithms was only slightly less accurate than the combination of individual humans with all seven algorithms. The balance of man and machine contributions to the pairwise fusions varied widely, indicating that a one-size-fits-all weighting of human and machine face recognition estimates is not appropriate.