Face Recognition and Drunk Classification Using Infrared Face Images
| Autor: | Francisco Pizarro, Margarita Machuca, Jose Luis Verdugo, Gonzalo Farias, Esteban Vera, Gabriel Hermosilla |
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| Rok vydání: | 2018 |
| Předmět: |
Article Subject
Computer science Local binary patterns ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Weber local descriptor Two stages Facial recognition system lcsh:Technology (General) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation 021110 strategic defence & security studies business.industry Pattern recognition Mixture model Linear discriminant analysis ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering lcsh:T1-995 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Curse of dimensionality |
| Zdroj: | Journal of Sensors, Vol 2018 (2018) |
| ISSN: | 1687-7268 1687-725X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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