A Model for Inebriation Recognition in Humans Using Computer Vision

Zibusiso Bhango, Dustin van der Haar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


The cost of substance use regarding lives lost, medical and psychiatric morbidity and social disruptions by far surpasses the economic costs. Alcohol abuse and dependence has been a social issue in need of addressing for centuries now. Methods exist that attempt to solve this problem by recognizing inebriation in humans. These methods include the use of blood tests, breathalyzers, urine tests, ECGs and wearables devices. Although effective, these methods are very inconvenient for the user, and the required equipment is expensive. We propose a method that provides a faster and convenient way to recognize inebriation. Our method uses Viola-Jones-based face-detection for the region of interest. The face images become input to a Convolutional Neural Network (CNN) which attempts to classify inebriation. In order to test our model’s performance against other methods, we implemented Local Binary Patterns (LBP) for feature extraction, and Support Vector Machines (SVM), Gaussian Naive Bayes (GNB) and k-Nearest Neighbor (kNN) classifiers. Our model had an accuracy rate of 84.31% and easily outperformed the other methods.

Original languageEnglish
Title of host publicationBusiness Information Systems - 22nd International Conference, BIS 2019, Proceedings
EditorsWitold Abramowicz, Rafael Corchuelo
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030204846
Publication statusPublished - 2019
Event22nd International Conference on Business Information Systems, BIS 2019 - Seville, Spain
Duration: 26 Jun 201928 Jun 2019

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356


Conference22nd International Conference on Business Information Systems, BIS 2019


  • Computer vision
  • Convolutional Neural Networks
  • Inebriation recognition
  • k-Nearest Neighbor
  • Machine learning
  • Naive Bayes
  • Support Vector Machines

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Business and International Management
  • Information Systems
  • Modeling and Simulation
  • Information Systems and Management


Dive into the research topics of 'A Model for Inebriation Recognition in Humans Using Computer Vision'. Together they form a unique fingerprint.

Cite this