A smile can reveal your age: enabling facial dynamics in age estimation
Proceedings of the 20th ACM international conference on Multimedia
Are you really smiling at me? spontaneous versus posed enjoyment smiles
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Facial landmarking: comparing automatic landmarking methods with applications in soft biometrics
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Face registration: evaluating generative models for automatic dense landmarking of the face
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Hybrid method based on topography for robust detection of iris center and eye corners
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn–Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn–Kanade and BU-4DFE).