A Model for Inebriation Recognition in Humans Using Computer Vision

  • Zibusiso Bhango
  • Dustin van der HaarEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)


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.


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


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Academy of Computer Science and Software EngineeringUniversity of JohannesburgJohannesburgSouth Africa

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