2018 |
da Silva, Samira Santos Aggregating Partial Least Squares Models for Open-set Face Identification Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Face Identification, Open-set Face Recognition, Partial Least Squares @mastersthesis{Silva:2018:MSc,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. |
2018 |
Samira Santos da Silva Aggregating Partial Least Squares Models for Open-set Face Identification Masters Thesis Federal University of Minas Gerais, 2018. Resumo | Links | BibTeX | Tags: Face Identification, Open-set Face Recognition, Partial Least Squares @mastersthesis{Silva:2018:MSc,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. |