OPEN ACCESS
The proposed framework combines machine learning techniques and Riemannian geometry-based shape analysis. We represent facial surfaces by collections of radial curves and iso-level curves, the shapes of corresponding curves are compared using a Riemmannian framework. We select the most discriminative curves using the well known AdaBoost algorithm. The experiment involving FRGC v2 dataset demonstrates the effectiveness of this feature selection by achieving 98.02 % as rank-1 recognition rate.
RÉSUMÉ
Dans cet article nous proposons de coupler la géométrie riemannienne avec les techniques d’apprentissage pour une biométrie faciale efficace et robuste aux changements d’expressions faciales. Nous représentons localement la forme des surfaces faciales par des collections de courbes 3D. Nous appliquons des techniques d’apprentissage afin de déterminer les courbes les plus pertinentes à la reconnaissance d’identité des personnes. Le taux de reconnaissance de l’ordre de 98, 02 % sur le benchmark de référence FRGC v2 confirme l’efficacité de coupler l’analyse géométrique de la forme avec des techniques d’apprentissage.
geometric features, riemannian geometry, geodesic path, facial curves, AdaBoost
MOTS-CLÉS
géométrie riemannienne, chemin géodésique, courbes faciales, AdaBoost
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