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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Boosting 3-D-geometric features for efficient face recognition and gender classification
IEEE Transactions on Information Forensics and Security, Volume 7, No. 6, Article 6247504, Year 2012
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Description
We utilize ideas from two growing but disparate ideas in computer visionshape analysis using tools from differential geometry and feature selection using machine learningto 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. © 2012 IEEE.
Authors & Co-Authors
Ballihi, Lahoucine
France, Villeneuve-d'ascq
Centre de Recherche en Informatique, Signal et Automatique de Lille Cristal
Morocco, Rabat
Faculté Des Sciences Rabat
Amor, Boulbaba Ben
France, Paris
Institut Mines Télécom
Daoudi, Mohamed
France, Paris
Institut Mines Télécom
Srivastava, Anuj
United States, Tallahassee
Florida State University
Aboutajdine, Driss
Morocco, Rabat
Faculté Des Sciences Rabat
Statistics
Citations: 92
Authors: 5
Affiliations: 4
Identifiers
Doi:
10.1109/TIFS.2012.2209876
ISSN:
15566013