Ibtissam Saadi, Douglas W. Cunningham, Taleb-ahmed Abdelmalik, Abdenour Hadid, Yassin El Hillali
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Accepted for publication in The 6th IEEE International Conference on Artificial Intelligence Circuits and Systems (IEEE AICAS 2024), 5 pages, 3 figures
Existing methods for driver facial expression recognition (DFER) are often computationally intensive, rendering them unsuitable for real-time applications. In this work, we introduce a novel transfer learning-based dual architecture, named ShuffViT-DFER, which elegantly combines computational efficiency and accuracy. This is achieved by harnessing the strengths of two lightweight and efficient models using convolutional neural network (CNN) and vision transformers (ViT). We efficiently fuse the extracted features to enhance the performance of the model in accurately recognizing the facial expressions of the driver. Our experimental results on two benchmarking and public datasets, KMU-FED and KDEF, highlight the validity of our proposed method for real-time application with superior performance when compared to state-of-the-art methods.
China University of Geosciences (Wuhan)（中国地质大学（武汉））；The University of Sydney（悉尼大学）；Jingdong（京东）
The recent success of Transformer has provided a new direction to various visual understanding tasks, including video-based facial expression recognition (FER). By modeling visual relations effectively, Transformer has shown its power for describing complicated patterns. However, Transformer still performs unsatisfactorily to notice subtle facial expression movements, because the expression movements of many videos can be too small to extract meaningful spatial-temporal relations and achieve robust performance. To this end, we propose to decompose each video into a series of expression snippet…
Chongqing University（重庆大学）；University of Waterloo（滑铁卢大学）；Shenzhen University（深圳大学）
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specific emotion, each participant completed the driving tasks in the same driving scenario and rated their emotional responses during the driving processes from the aspects of dimensional emotion and discrete emotion. We also conducted classification experiments to recognize the scales of arousal, valence, dominance, …
Purdue University（普渡大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Texas A&M University-Kingsville（德克萨斯农工大学金斯维尔分校）；University of Michigan（密歇根大学）
Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then re-formulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a…
Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints
Istituto Italiano di Tecnologia（意大利技术研究院）；Imperial College London（帝国理工学院）；Italian National Institute for Insurance against Accidents at Work (INAIL)（意大利国家工伤事故保险研究所（INAIL））
For real-world driver drowsiness detection from videos, the variation of head pose is so large that the existing methods on global face is not capable of extracting effective features, such as looking aside and lowering head. Temporal dependencies with variable length are also rarely considered by the previous approaches, e.g., yawning and speaking. In this paper, we propose a Long-term Multi-granularity Deep Framework to detect driver drowsiness in driving videos containing the frontal faces. The framework includes two key components: (1) Multi-granularity Convolutional Neural Network (MCNN),…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）；Pontifical Catholic University of Paraná（巴拉那天主教大学）
Automatic facial expression recognition is an important research area in the emotion recognition and computer vision. Applications can be found in several domains such as medical treatment, driver fatigue surveillance, sociable robotics, and several other human-computer interaction systems. Therefore, it is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. In recent years, deep neural networks have been used with great success in recognizing emotions. In this paper, we present a new model for continuous emotion recognition based on facial …
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Istanbul Technical University（伊斯坦布尔理工大学）
Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. However, learning from synthetic face images can be problematic due to the distribution discrepancy between low-quality synthetic images and real face images and may not achieve the desired performance when the learned model applies to real world scenarios. To this end, we propose a new attribute guided face image synthesis to perform a trans…
Karunya Institute of Technology and Sciences（卡鲁尼亚理工学院）；Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the…
Driving in a state of drowsiness is a major cause of road accidents, resulting in tremendous damage to life and property. Developing robust, automatic, real-time systems that can infer drowsiness states of drivers has the potential of making life-saving impact. However, developing drowsiness detection systems that work well in real-world scenarios is challenging because of the difficulties associated with collecting high-volume realistic drowsy data and modeling the complex temporal dynamics of evolving drowsy states. In this paper, we propose a data collection protocol that involves outfittin…
A key step to driver safety is to observe the driver's activities with the face being a key step in this process to extracting information such as head pose, blink rate, yawns, talking to passenger which can then help derive higher level information such as distraction, drowsiness, intent, and where they are looking. In the context of driving safety, it is important for the system perform robust estimation under harsh lighting and occlusion but also be able to detect when the occlusion occurs so that information predicted from occluded parts of the face can be taken into account properly. This…
School of Automation, Southeast University（东南大学自动化学院）；School of Automation, Nanjing University of Posts and Telecommunications（南京邮电大学自动化学院）
This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on lambertian model and local near neighbor feature of the face image, applying to local region within multiple scales. Then, the illumination level is referenced to construct the high performance LEF…
University of Arkansas（阿肯色大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Face identity masking algorithms developed in recent years aim to protect the privacy of people in video recordings. These algorithms are designed to interfere with identification, while preserving information about facial actions. An important challenge is to preserve subtle actions in the eye region, while obscuring the salient identity cues from the eyes. We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement, for interfering with identification and preserving facial actions. In Experiments 1 and 2, we tested human part…
Accurate face landmark localization is an essential part of face recognition, reconstruction and morphing. To accurately localize face landmarks, we present our heatmap regression approach. Each model consists of a MobileNetV2 backbone followed by several upscaling layers, with different tricks to optimize both performance and inference cost. We use five naïve face landmarks from a publicly available face detector to position and align the face instead of using the bounding box like traditional methods. Moreover, we show by adding random rotation, displacement and scaling -- after alignment --…
Driver inattention assessment has become a very active field in intelligent transportation systems. Based on active sensor Kinect and computer vision tools, we have built an efficient module for detecting driver distraction and recognizing the type of distraction. Based on color and depth map data from the Kinect, our system is composed of four sub-modules. We call them eye behavior (detecting gaze and blinking), arm position (is the right arm up, down, right of forward), head orientation, and facial expressions. Each module produces relevant information for assessing driver inattention. They …
School of Technologies, Cardiff Metropolitan University（卡迪夫城市大学技术学院）；Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology（维尔科技兰加拉詹博士萨贡塔拉科学与技术研发学院）
Driver drowsiness detection has been the subject of many researches in the past few decades and various methods have been developed to detect it. In this study, as an image-based approach with adequate accuracy, along with the expedite process, we applied YOLOv3 (You Look Only Once-version3) CNN (Convolutional Neural Network) for extracting facial features automatically. Then, LSTM (Long-Short Term Memory) neural network is employed to learn driver temporal behaviors including yawning and blinking time period as well as sequence classification. To train YOLOv3, we utilized our collected datase…
Dense facial landmark detection is one of the key elements of face processing pipeline. It is used in virtual face reenactment, emotion recognition, driver status tracking, etc. Early approaches were suitable for facial landmark detection in controlled environments only, which is clearly insufficient. Neural networks have shown an astonishing qualitative improvement for in-the-wild face landmark detection problem, and are now being studied by many researchers in the field. Numerous bright ideas are proposed, often complimentary to each other. However, exploration of the whole volume of novel a…
Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning
University of Canberra（堪培拉大学）；School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（Curtin大学电子工程、计算与数学科学学院）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电子与数据工程学院）
LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection
School of Computing Technologies, RMIT University（计算技术学院，皇家墨尔本理工大学）；CSIRO’s Data61（CSIRO的数据61）；School of Computer Science, University of Auckland（计算机科学学院，奥克兰大学）；STEM, University of South Australia（STEM，南澳大利亚大学）
Innopolis University（因诺波利斯大学）；Institute of Data Science & AI（数据科学与人工智能研究所）
Bringing empathy to a computerized system could significantly improve the quality of human-computer communications, as soon as machines would be able to understand customer intentions and better serve their needs. According to different studies (Literature Review), visual information is one of the most important channels of human interaction and contains significant behavioral signals, that may be captured from facial expressions. Therefore, it is consistent and natural that the research in the field of Facial Expression Recognition (FER) has acquired increased interest over the past decade du…
School of Automation, Southeast University（东南大学自动化学院）
Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark d…
Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales（布宜诺斯艾利斯大学，精确科学与自然学院）；Institute of Engineering Sciences, Universidad de O’Higgins（工程科学研究所，奥希金斯大学）；CONICET-UBA, Instituto de Ciencias de la Computacion (ICC)（CONICET-UBA，计算科学研究所）；L3S Research Center, Leibniz Universität Hannover（L3S研究中心，汉诺威莱布尼茨大学）
Edge Hill University（边山大学）；Aberystwyth University（阿伯里斯特威斯大学）
Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose "informativeness" can be evaluated by the…
Qatar Transportation and Traffic Safety Center, Qatar University（卡塔尔大学卡塔尔交通运输与交通安全中心）；Rutgers, The State University of New Jersey（罗格斯大学（新泽西州立大学））
Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA).…
