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Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance
March 2013 (vol. 35 no. 3)
pp. 624-638
E. Sangineto, Pattern Anal. & Comput. Vision (PAVIS), Ist. Italiano di Tecnol., Genoa, Italy
We present an approach to automatic localization of facial feature points which deals with pose, expression, and identity variations combining 3D shape models with local image patch classification. The latter is performed by means of densely extracted SURF-like features, which we call DU-SURF, while the former is based on a multiclass version of the Hausdorff distance to address local classification errors and nonvisible points. The final system is able to localize facial points in real-world scenarios, dealing with out of plane head rotations, expression changes, and different lighting conditions. Extensive experimentation with the proposed method has been carried out showing the superiority of our approach with respect to other state-of-the-art systems. Finally, DU-SURF features have been compared with other modern features and we experimentally demonstrate their competitive classification accuracy and computational efficiency.
Index Terms:
Face,Shape,Three dimensional displays,Vectors,Feature extraction,Detectors,efficient feature extraction,Facial feature detection,head pose estimation,Hausdorff distance
Citation:
E. Sangineto, "Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 624-638, March 2013, doi:10.1109/TPAMI.2012.87
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