FootSee: an interactive animation system
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Locating human faces within images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Models of Animals from Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition
Computer Vision and Image Understanding
Factors that influence algorithm performance in the Face Recognition Grand Challenge
Computer Vision and Image Understanding
FRVT 2006: Quo Vadis face quality
Image and Vision Computing
View-based eigenspaces with mixture of experts for view-independent face recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Every picture tells a story: generating sentences from images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Thermal face recognition in an operational scenario
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
False alarm rate: a critical performance measure for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
NB+: An improved Naïve Bayesian algorithm
Knowledge-Based Systems
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To obtain a quantitative assessment of the state of automatic face recognition, we performed a meta-analysis of performance results of face recognition algorithms in the literature. The analysis was conducted on 24 papers that report identification performance on frontal facial images and used either the FERET or ORL database in their experiments. The analysis shows that control scores are predictive of performance of novel algorithms at statistically significant levels. The analysis identified three methodological areas for improvement in automatic face recognition. First, the majority of papers report experimental results for face recognition problems that are already solved. Second, authors do not adequately document their experiments. Third, performance results for novel or experimental algorithms need to be accompanied by control algorithm performance scores.