  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.
