The task is to build a classifier that can classify for a given image as sober or mild drunk or drunk. In order to train or test, u can consider all four, front, side etc. at once to classify whether drunk or sober.
Attached is the data of 41 people, taken from IR sensor. For each person, there are 4 types of images taken: sober, 20 mins after drinking, 40 mins after drinking, 1hr after drinking 4 glasses of wine. For each type for each person, 4 images are taken: front face, side face, eyes, and hand palm.
I have used virtual environments for handling the dependencies. Run the following command:
pip install -r requirements.txt
- This repository explores and presents 2 different approaches to tackle the problem.
- Using transfer learning - based on concepts of deep learning - achieving an accuracy of 87.5% (More details inside drunk_classification_dl directory).
- Using rudimentary methods of feature extraction and separate classifier - based on classical machine learning - achieving an accuracy of 58% (More details inside drunk_classification_ml directory)