Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier

Simon Dobrišek, Vitomir Štruc, Janez Križaj, France Mihelič (2015): Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier. 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015, 2 , IEEE 2015.

Abstract

The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing.

BibTeX (Download)

@conference{dobrivsek2015face,
title = {Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier},
author = { Simon Dobri\v{s}ek and Vitomir \v{S}truc and Janez Kri\v{z}aj and France Miheli\v{c}},
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/Bwild2015.pdf},
year  = {2015},
date = {2015-01-01},
booktitle = {11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015},
volume = {2},
pages = {1--6},
organization = {IEEE},
abstract = {The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing.},
keywords = {biometrics, BWild, FG, Gabor features, PaSC, plda, probabilistic Gabor Fisher classifier, probabilistic linear discriminant analysis},
pubstate = {published},
tppubtype = {conference}
}