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Processing and analysis of 2.5D face models for non-rigid mapping based face recognition using differential geometry toolsSzeptycki, Przemyslaw 06 July 2011 (has links) (PDF)
This Ph.D thesis work is dedicated to 3D facial surface analysis, processing as well as to the newly proposed 3D face recognition modality, which is based on mapping techniques. Facial surface processing and analysis is one of the most important steps for 3Dface recognition algorithms. Automatic anthropometric facial features localization also plays an important role for face localization, face expression recognition, face registration ect., thus its automatic localization is a crucial step for 3D face processing algorithms. In this work we focused on precise and rotation invariant landmarks localization, which are later used directly for face recognition. The landmarks are localized combining local surface properties expressed in terms of differential geometry tools and global facial generic model, used for face validation. Since curvatures, which are differential geometry properties, are sensitive to surface noise, one of the main contributions of this thesis is a modification of curvatures calculation method. The modification incorporates the surface noise into the calculation method and helps to control smoothness of the curvatures. Therefore the main facial points can be reliably and precisely localized (100% nose tip localization using 8 mm precision)under the influence of rotations and surface noise. The modification of the curvatures calculation method was also tested under different face model resolutions, resulting in stable curvature values. Finally, since curvatures analysis leads to many facial landmark candidates, the validation of which is time consuming, facial landmarks localization based on learning technique was proposed. The learning technique helps to reject incorrect landmark candidates with a high probability, thus accelerating landmarks localization. Face recognition using 3D models is a relatively new subject, which has been proposed to overcome shortcomings of 2D face recognition modality. However, 3Dface recognition algorithms are likely more complicated. Additionally, since 3D face models describe facial surface geometry, they are more sensitive to facial expression changes. Our contribution is reducing dimensionality of the input data by mapping3D facial models on to 2D domain using non-rigid, conformal mapping techniques. Having 2D images which represent facial models, all previously developed 2D face recognition algorithms can be used. In our work, conformal shape images of 3Dfacial surfaces were fed in to 2D2 PCA, achieving more than 86% recognition rate rank-one using the FRGC data set. The effectiveness of all the methods has been evaluated using the FRGC and Bosphorus datasets.
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Processing and analysis of 2.5D face models for non-rigid mapping based face recognition using differential geometry tools / Traitement et analyse des modèles 2.5 de visage utilisant les outils de la géométrie différentielle pour la reconnaissance faciale basée sur l'appariement non rigideSzeptycki, Przemyslaw 06 July 2011 (has links)
Ce travail de thèse concerne l’analyse de surfaces faciales en 3D, ainsi que leur traitement, dans le récent cadre de la modalité de reconnaissance de visages en 3D,basé sur des techniques d’appariement. Le traitement de la surface faciale et son analyse constituent une étape importante dans les algorithmes de reconnaissance de visage en 3D. La localisation de points d’intérêt anthropométriques du visage joue par ailleurs un rôle important dans les techniques de localisation du visage, de reconnaissance d’expression, de recalage, etc. Ainsi, leur localisation automatique joue un rôle crucial dans les algorithmes de traitement du visage 3D. Dans ce travail, nous avons mis l’accent sur la localisation précise et invariante en rotation des points d’intérêt, qui seront utilisés plus tard pour la reconnaissance de visages. Ces points d’intérêt sont localisés en combinant les propriétés locales de la surface faciale, exprimées en termes de géométrie différentielle, et un modèle global et générique du visage. Etant donné que la sensibilité des courbures, qui sont des propriétés de géométrie différentielle, au bruit, une des contributions de cette thèse est la modification d’une méthode de calcul de courbures. Cette modification incorpore le bruit de la surface dans la méthode de calcul, et permet de contrôler la progressivité des courbures. Par conséquent, nous pouvons localiser les points d’intérêt de la surface faciale avec précision et fiabilité (100% de bonnes localisation du bout du nez avec une erreur maximale de 8mmpar exemple) y compris en présence de rotations et de bruit. La modification de la méthode de calcul de courbure a été également testée pour différentes résolutions de visage, présentant des valeurs de courbure stables. Enfin, étant donné que donné que l’analyse de courbures mène à de nombreux candidats de points d’intérêt du visage, dont la validation est coûteuse, nous proposons de localiser les points d’intérêt grâce à une méthode d’apprentissage. Cette méthode permet de rejeter précocement des faux candidats avec une grande confiance, accélérant d’autant la localisation des points d’intérêt. La reconnaissance de visages à l’aide de modèles 3D est un sujet relativement nouveau, qui a été propose pour palier aux insuffisantes de la modalité de reconnaissance de visages en 2D. Cependant, les algorithmes de reconnaissance de visage en 3D sont généralement plus complexes. De plus, étant donné que les modèles de visage 3D décrivent la géométrie du visage, ils sont plus sensibles que les images 2Dde texture aux expressions faciales. Notre contribution est de réduire la dimensionnalité des données de départ en appariant les modèles de visage 3D au domaine 2Dà l’aide de méthodes, non rigides, d’appariement conformal. L’existence de modèles2D représentant les visages permet alors d’utiliser les techniques précédemment développées dans le domaine de la reconnaissance de visages en 2D. Dans nos travaux, nous avons utilisé les cartes conformales de visages 3D en conjonction avec l’algorithme2D2 PCA, atteignant le score de 86% en reconnaissance de rang 1 sur la base de données FRGC. L’efficacité de toutes les méthodes a été évaluée sur les bases FRGC et Bosphorus. / This Ph.D thesis work is dedicated to 3D facial surface analysis, processing as well as to the newly proposed 3D face recognition modality, which is based on mapping techniques. Facial surface processing and analysis is one of the most important steps for 3Dface recognition algorithms. Automatic anthropometric facial features localization also plays an important role for face localization, face expression recognition, face registration ect., thus its automatic localization is a crucial step for 3D face processing algorithms. In this work we focused on precise and rotation invariant landmarks localization, which are later used directly for face recognition. The landmarks are localized combining local surface properties expressed in terms of differential geometry tools and global facial generic model, used for face validation. Since curvatures, which are differential geometry properties, are sensitive to surface noise, one of the main contributions of this thesis is a modification of curvatures calculation method. The modification incorporates the surface noise into the calculation method and helps to control smoothness of the curvatures. Therefore the main facial points can be reliably and precisely localized (100% nose tip localization using 8 mm precision)under the influence of rotations and surface noise. The modification of the curvatures calculation method was also tested under different face model resolutions, resulting in stable curvature values. Finally, since curvatures analysis leads to many facial landmark candidates, the validation of which is time consuming, facial landmarks localization based on learning technique was proposed. The learning technique helps to reject incorrect landmark candidates with a high probability, thus accelerating landmarks localization. Face recognition using 3D models is a relatively new subject, which has been proposed to overcome shortcomings of 2D face recognition modality. However, 3Dface recognition algorithms are likely more complicated. Additionally, since 3D face models describe facial surface geometry, they are more sensitive to facial expression changes. Our contribution is reducing dimensionality of the input data by mapping3D facial models on to 2D domain using non-rigid, conformal mapping techniques. Having 2D images which represent facial models, all previously developed 2D face recognition algorithms can be used. In our work, conformal shape images of 3Dfacial surfaces were fed in to 2D2 PCA, achieving more than 86% recognition rate rank-one using the FRGC data set. The effectiveness of all the methods has been evaluated using the FRGC and Bosphorus datasets.
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