  Datasets are crucial when training a deep neural network. When datasets are
unrepresentative, trained models are prone to bias because they are unable to
generalise to real world settings. 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 particular challenge for Driver
drowsiness detection, where many publicly available datasets are
unrepresentative as they cover only certain ethnicity groups. Traditional
augmentation methods are unable to improve a model's performance when tested on
other groups with different facial attributes, and it is often challenging to
build new, more representative datasets. In this paper, we introduce a novel
framework that boosts the performance of detection of drowsiness for different
ethnicity groups. Our framework improves Convolutional Neural Network (CNN)
trained for prediction by using Generative Adversarial networks (GAN) for
targeted data augmentation based on a population bias visualisation strategy
that groups faces with similar facial attributes and highlights where the model
is failing. A sampling method selects faces where the model is not performing
well, which are used to fine-tune the CNN. Experiments show the efficacy of our
approach in improving driver drowsiness detection for under represented
ethnicity groups. Here, models trained on publicly available datasets are
compared with a model trained using the proposed data augmentation strategy.
Although developed in the context of driver drowsiness detection, the proposed
framework is not limited to the driver drowsiness detection task, but can be
applied to other applications.
