Sobriety Testing Based on Thermal Infrared Images Using Convolutional Neural Networks

Aditya K. Kamath, A. Tarun Karthik, Leslie Monis, Manjunath Mulimani, Shashidhar G. Koolagudi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This paper proposes a method to test the sobriety of an individual using infrared images of the persons eyes, face, hand, and facial profile. The database we used consisted of images of forty different individuals. The process is broken down into two main stages. In the first stage, the data set was divided according to body part and each one was run through its own Convolutional Neural Network (CNN). We then tested the resulting network against a validation data set. The results obtained gave us an indication of which body parts were better suited for identifying signs of drunken state and sobriety. In the second stage, we took the weights of CNN giving best validation accuracy from the first stage. We then grouped the body parts according to the person they belong to. The body parts were fed together into a CNN using the weights obtained in the first stage. The result for each body part was passed to a simple back-propagation neural network (BPNN) to get final results. We tried to identify the most optimal configuration of neural networks for each stage of the process. The results we obtained showed that facial profile images tend to give very good indications of sobriety. The results also showed that combining the results of multiple body parts using a simple BPNN gives a higher accuracy than that of individual ones.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538654576
Publication statusPublished - 22-02-2019
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 28-10-201831-10-2018

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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