Abstract
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).
Original language | English |
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Article number | 5963714 |
Pages (from-to) | 844-858 |
Number of pages | 15 |
Journal | IEEE Transactions on Image Processing |
Volume | 21 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Feb 2012 |
Keywords
- Facial feature localization
- facial landmarking
- factor analysis
- Gabor wavelet features
- mixture models
- shape prior
- structural analysis