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Abstract
This paper proposes a methodology to assess the discriminative capabilities of geometrical descriptors referring to the public Bosphorus 3D facial database as testing dataset. The investigated descriptors include histogram versions of Shape Index and Curvedness, Euclidean and geodesic distances between facial soft-tissue landmarks. The discriminability of these features is evaluated through the analysis of single block of features and their meanings with different techniques. Multilayer perceptron neural network methodology is adopted to evaluate the relevance of the features, examined in different test combinations. Principle Component Analysis (PCA) is applied for dimensionality reduction.
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Authors and Affiliations
DIGEP, Politecnico Di Torino, Turin, Italy
F. Marcolin, S. Spada & E. Vezzetti
Laboratory LTI, Université de Picardie Jules Verne, Amiens, France
G. Cirrincione
University of South Pacific, Suva, Fiji
G. Cirrincione
- G. Cirrincione
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- F. Marcolin
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- S. Spada
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- E. Vezzetti
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Correspondence toS. Spada.
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Editors and Affiliations
Dipartimento di Psicologia, Università della Campana Luigi Vanvitelli, Caserta, Italy
Anna Esposito
Fundació Tecnocampus, Pompeu Fabra University, Mataro, Barcelona, Spain
Marcos Faundez-Zanuy
Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
Francesco Carlo Morabito
Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
Eros Pasero
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Cirrincione, G., Marcolin, F., Spada, S., Vezzetti, E. (2019). Intelligent Quality Assessment of Geometrical Features for 3D Face Recognition. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_14
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