The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Sketch Synthesis and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
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
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Demographic effects on estimates of automatic face recognition performance
Image and Vision Computing
Comparing face recognition algorithms to humans on challenging tasks
ACM Transactions on Applied Perception (TAP)
I see you there!: developing identity-preserving embodied interaction for museum exhibits
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Psychological research indicates that humans recognize faces of their own race more accurately than faces of other races. This “other-race effect” occurs for algorithms tested in a recent international competition for state-of-the-art face recognition algorithms. We report results for a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made by fusing five algorithms from East Asian countries. At the low false accept rates required for most security applications, the Western algorithm recognized Caucasian faces more accurately than East Asian faces and the East Asian algorithm recognized East Asian faces more accurately than Caucasian faces. Next, using a test that spanned all false alarm rates, we compared the algorithms with humans of Caucasian and East Asian descent matching face identity in an identical stimulus set. In this case, both algorithms performed better on the Caucasian faces—the “majority” race in the database. The Caucasian face advantage, however, was far larger for the Western algorithm than for the East Asian algorithm. Humans showed the standard other-race effect for these faces, but showed more stable performance than the algorithms over changes in the race of the test faces. State-of-the-art face recognition algorithms, like humans, struggle with “other-race face” recognition.