Title: Viewpoint invariant gender recognition

Authors: Mokhtar Taffar; Serge Miguet; Mohammed Benmohammed

Addresses: Computer Science Department, University of Jijel, BP 98, Ouled Aissa, 18000, Jijel, Algeria ' LIRIS, CNRS UMR 5205, Université Lyon 2, 5, av. Mendès-France, Bât. C, 69676, Bron, Lyon, France ' LIRE Lab., University of Constantine, BP 325, Route Ain-El-Bey, 25017, Constantine, Algeria

Abstract: In this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localisation based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localisation and gender classification experiments on PIE, FERET and CMU-profiles databases. The experimental results show that the probabilistic invariant model performs well to detect face and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces.

Keywords: local features; invariant descriptors; object appearance; modelling; probabilistic models; learning models; face detection; gender classification; pattern recognition; viewpoint invariant; gender recognition; face localisation.

DOI: 10.1504/IJAPR.2013.052341

International Journal of Applied Pattern Recognition, 2013 Vol.1 No.1, pp.47 - 60

Received: 08 Sep 2012
Accepted: 27 Sep 2012
Published online: 31 Jul 2014 *

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