@mastersthesis{Silva:2018:MSc,
title = {Aggregating Partial Least Squares Models for Open-set Face Identification},
author = {Samira Santos da Silva},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2020/04/dissertation_versaocomfichacatalografica.pdf},
year = {2018},
date = {2018-01-16},
school = {Federal University of Minas Gerais},
abstract = {Face identification is an important task in computer vision and has a myriad of
applications, such as in surveillance, forensics and human-computer interaction. In the
past few years, several methods have been proposed to solve face identification task in
closed-set scenarios, that is, methods that make assumption of all the probe images
necessarily matching a gallery individual. However, in real-world applications, one
might want to determine the identity of an unknown face in open-set scenarios. In this
work, we propose a novel method to perform open-set face identification by aggregating
Partial Least Squares models using the one-against-all protocol in a simple but fast
way. The model outputs are combined into a response histogram which is balanced
if the probe face belongs to a gallery individual or have a highlighted bin, otherwise.
Evaluation is performed in four datasets: FRGCv1, FG-NET, Pubfig and Pubfig83.
Results show significant improvement when compared to state-of-the art approaches
regardless challenges posed by different datasets.},
keywords = {Face Identification, Open-set Face Recognition, Partial Least Squares},
pubstate = {published},
tppubtype = {mastersthesis}
}