TY - GEN
T1 - A Model for Inebriation Recognition in Humans Using Computer Vision
AU - Bhango, Zibusiso
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computer vision
KW - Convolutional Neural Networks
KW - Inebriation recognition
KW - k-Nearest Neighbor
KW - Machine learning
KW - Naive Bayes
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=85068118271&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20485-3_20
DO - 10.1007/978-3-030-20485-3_20
M3 - Conference contribution
AN - SCOPUS:85068118271
SN - 9783030204846
T3 - Lecture Notes in Business Information Processing
SP - 259
EP - 270
BT - Business Information Systems - 22nd International Conference, BIS 2019, Proceedings
A2 - Abramowicz, Witold
A2 - Corchuelo, Rafael
PB - Springer Verlag
T2 - 22nd International Conference on Business Information Systems, BIS 2019
Y2 - 26 June 2019 through 28 June 2019
ER -
