Correcting inter-sectional accuracy differences in drowsiness detection systems using generative adversarial networks (GANs)
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Date
2020
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Abstract
oad accidents contribute to many injuries and deaths among the human population. There is
substantial evidence that proves drowsiness is one of the most prominent causes of road accidents
all over the world. This results in fatalities and severe injuries for drivers, passengers, and
pedestrians. These alarming facts are raising the interest in equipping vehicles with robust driver
drowsiness detection systems to minimise accident rates. One of the primary concerns of motor
industries is the safety of passengers and as a consequence they have invested significantly in
research and development to equip vehicles with systems that can help minimise to road accidents.
A number research endeavours have attempted to use Artificial intelligence, and particularly Deep
Neural Networks (DNN), to build intelligent systems that can detect drowsiness automatically.
However, datasets are crucial when training a DNN. When datasets are unrepresentative, trained
models are prone to bias because they are unable to generalise. This is particularly problematic
for models trained in specific cultural contexts, which may not represent a wide range of races,
and thus fail to generalise. This is a specific challenge for driver drowsiness detection task,
where most publicly available datasets are unrepresentative as they cover only certain ethnicity
groups. This thesis investigates the problem of an unrepresentative dataset in the training
phase of Convolutional Neural Networks (CNNs) models. Firstly, CNNs are compared with
several machine learning techniques to establish their superior suitability for the driver drowsiness
detection task. An investigation into the implementation of CNNs was performed and highlighted
that publicly available datasets such as NTHU, DROZY and CEW do not represent a wide
spectrum of ethnicity groups and lead to biased systems. A population bias visualisation technique
was proposed to help identify the regions, or individuals where a model is failing to generalise
on a picture grid. Furthermore, the use of Generative Adversarial Networks (GANs) with
lightweight convolutions called Depthwise Separable Convolutions (DSC) for image translation
to multi-domain outputs was investigated in an attempt to generate synthetic datasets. This
thesis further showed that GANs can be used to generate more realistic images with varied facial
attributes for predicting drowsiness across multiple ethnicity groups. Lastly, a novel framework
was developed to detect bias and correct it using synthetic generated images which are produced
by GANs. Training models using this framework results in a substantial performance boost.
Description
Doctoral Degrees. University of KwaZulu-Natal, Durban.