TY - JOUR
T1 - Face Recognition and Drunk Classification Using Infrared Face Images
AU - Hermosilla, Gabriel
AU - Verdugo, José Luis
AU - Farias, Gonzalo
AU - Vera, Esteban
AU - Pizarro, Francisco
AU - Machuca, Margarita
N1 - Funding Information:
This work was supported in part by FONDECYT under Grant 11130466, Grant 1161584, and Grant 11150476 and in part by Pontificia Universidad Católica de Valparaíso DI Regular Code under Grant 039.420/2017.
Publisher Copyright:
© 2018 Gabriel Hermosilla et al.
PY - 2018
Y1 - 2018
N2 - The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a "DrunkSpace Classifier." The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.
AB - The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a "DrunkSpace Classifier." The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.
UR - http://www.scopus.com/inward/record.url?scp=85042166078&partnerID=8YFLogxK
U2 - 10.1155/2018/5813514
DO - 10.1155/2018/5813514
M3 - Article
AN - SCOPUS:85042166078
VL - 2018
JO - Journal of Sensors
JF - Journal of Sensors
SN - 1687-725X
M1 - 5813514
ER -