@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}
}

@mastersthesis{Santos:2015:MSc,
title = {Partial Least Squares for Face Hashing},
author = {Cassio Elias Santos dos Junior},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/dissertation_2015_Cassio.pdf},
year = {2015},
date = {2015-08-24},
school = {Federal University of Minas Gerais},
abstract = {Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature.},
keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+},
pubstate = {published},
tppubtype = {mastersthesis}
}