IEEE Trans Pattern Anal Mach Intell - Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance.

Tópicos

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Resumo

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.

Resumo Limpo

present approach automat local facial featur point deal pose express ident variat combin d shape model local imag patch classif latter perform mean dens extract surflik featur call dusurf former base multiclass version hausdorff distanc address local classif error nonvis point final system abl local facial point realworld scenario deal plane head rotat express chang differ light condit extens experiment propos method carri show superior approach respect stateoftheart system final dusurf featur compar modern featur experiment demonstr competit classif accuraci comput effici

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