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Boosting 3-D-Geometric Features for Efficient Face Recognition and Gender Classification

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5 Author(s)
Lahoucine Ballihi ; Laboratoire d'Informatique Fondamentale de Lille (UMR CNRS/Lille 8022), Villeneuve d'Ascq Cedex France and LRIT, Unité Associée au CNRST (URAC 29), Faculté des Sciences, Université Mohammed V¿Agdal, Rabat, Maroc ; Boulbaba Ben Amor ; Mohamed Daoudi ; Anuj Srivastava
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We utilize ideas from two growing but disparate ideas in computer vision-shape analysis using tools from differential geometry and feature selection using machine learning-to select and highlight salient geometrical facial features that contribute most in 3-D face recognition and gender classification. First, a large set of geometries curve features are extracted using level sets (circular curves) and streamlines (radial curves) of the Euclidean distance functions of the facial surface; together they approximate facial surfaces with arbitrarily high accuracy. Then, we use the well-known Adaboost algorithm for feature selection from this large set and derive a composite classifier that achieves high performance with a minimal set of features. This greatly reduced set, consisting of some level curves on the nose and some radial curves in the forehead and cheeks regions, provides a very compact signature of a 3-D face and a fast classification algorithm for face recognition and gender selection. It is also efficient in terms of data storage and transmission costs. Experimental results, carried out using the FRGCv2 dataset, yield a rank-1 face recognition rate of 98% and a gender classification rate of 86% rate.

Published in:

IEEE Transactions on Information Forensics and Security  (Volume:7 ,  Issue: 6 )
IEEE Biometrics Compendium
IEEE RFIC Virtual Journal
IEEE RFID Virtual Journal