Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

Abstract

Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4%. In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark-skinned drivers faces. This is a particularly important challenge in African contexts, where there are more dark-skinned drivers. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. This work evaluates the performance obtained when training convolutional neural network models on commonly used driver drowsiness detection datasets and testing on datasets specifically chosen for broader representation. Results show that models trained using publicly available datasets suffer extensively from over-fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

“…However, their proposed methodology still requires that some training data be available for various population groups. A novel visualization technique which can be assistance to identify groups of people was proposed by research in [6] where potential discrimination could arise due to the usage of Principal Component Analysis (PCA). They used PCA to produce a grid of faces sorted by similarity and combining these with a model accuracy overlay.…”

“…Driver facial features based detection methods are based on facial features using various methods, i.e. CNN based Deep Learning Model [4],Generative Adversial Networks (GAN) [5],Principal Component Analysis [6], DriCare [7] where analysis was done on pupil, eyelids and head pose to detect drowsiness. This research proposes a robust method for vehicle driver drowsiness detection using facial features based head orientation and pupil detection where frame aggregation strategy is also used to ensure facial features processing under challenging circumstances, i.e.…”

“…However, due to the lack of sufficient training data for various population groups, their research requires further investigation. Research in [6] showed that there are potential social challenges regarding the application of drowsiness detection by highlighting problems in detecting dark-skinned driver faces. They focused to use unrepresentative images to train driver drowsiness detection system using vision based approach.…”

Bias Remediation in Driver Drowsiness Detection Systems Using Generative Adversarial Networks

“…For African contexts, this poses a challenge since the population is diverse and individuals can have many different facial attributes. Models trained using publicly available datasets do not generalise well in an African context [18].…”

“…The limitations of datasets that fail to cover a wide range of ethnicities lead to bias in trained models when it comes to contexts with different nationalities. Prior work has shown that visualisation techniques can be used to identify bias in training datasets by identifying population groups where a classifier tends to fail [18]. This paper makes the following contributions in addressing population bias in driver drowsiness training datasets:…”