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

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5 Author(s)
Ballihi, L. ; Lab. d''Inf. Fondamentale de Lille, Villeneuve-d''Ascq, France ; Ben Amor, B. ; Daoudi, M. ; Srivastava, A.
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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:

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

Date of Publication:

Dec. 2012

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