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Journal of Sensors
Volume 2018 (2018), Article ID 5813514, 8 pages
Research Article

Face Recognition and Drunk Classification Using Infrared Face Images

1Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
2Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
3Universidad de Santiago de Chile, Santiago, Chile

Correspondence should be addressed to Gonzalo Farias

Received 25 April 2017; Revised 10 July 2017; Accepted 3 December 2017; Published 30 January 2018

Academic Editor: Paolo Mercorelli

Copyright © 2018 Gabriel Hermosilla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.