DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification

TL;DR

This paper introduces DIF, a novel audio-visual dataset for detecting intoxicated individuals, and proposes bimodal deep learning methods for non-invasive intoxication detection to improve road safety.

Contribution

The work presents the first dataset of perceived intoxicated faces and develops bimodal CNN and DNN models for automatic intoxication detection.

Findings

CNN and DNN baselines established for audio-visual data

3D CNN captures spatio-temporal video features

Proposed non-linearity variation enhances 3D convolution effectiveness

Abstract

Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.