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Facial Thermal Image Analysis with Deep Convolutional Neural Network Architectures for Subject Dependent Drunkenness Diagnosis

This paper presents a new approach to subject dependent drunkenness diagnosis using Deep Convolutional Neural Networks (DCNNs) to process facial thermal images. Three different DCNN architectures are proposed for this research; the first module has 15 layers, the second one is composed of 7 layers and the last one is GoogLeNet, composed of 22 layers and trained using transfer learning. The proposed DCNNs are evaluated using a large dataset that contains facial thermal infrared images of 41 subjects. The dataset contains 100 thermal images for each subject. Half of them corresponds to the sober condition of the subject, obtained before alcohol consumption, and the other half for the drunkenness condition, obtained 1 hour after each subject drank 4 glasses of wine. The experimental results clearly show that DCNNs produce good results when used to process facial thermal images. The detection scores obtained had their lowest value 93.17% and the highest value 98.54%.