A Robust Driver Emotion Recognition Method Based on High-Purity Feature Separation

Yang, Lie; Yang, Haohan; Hu, Bin-Bin; Wang, Yan; Lv, Chen

doi:10.1109/tits.2023.3304128

Cited by 16 publications

(3 citation statements)

References 49 publications

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“…Our model, on the other hand, has been experimented under three different lighting conditions and We will show achieved significant performance gains in all of them, the results of the performance comparison with the existing models under strong lighting environments, as shown in Table 5. Compared to HPFS [42], which uses feature separation as a way to overcome the interference of light variations, our method of local relevance learning using the CoT module is more advantageous. Results on RAF-DB: Table 6 shows that our accuracy on the RAF-DB dataset is 91.07%, which outperforms other models.…”

Section: Comparision Studymentioning

confidence: 99%

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Xiong,

Zhang,

Zheng

et al. 2024

Applied Sciences

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In real-world scenarios, the facial expression recognition task faces several challenges, including lighting variations, image noise, face occlusion, and other factors, which limit the performance of existing models in dealing with complex situations. To cope with these problems, we introduce the CoT module between the CNN and ViT frameworks, which improves the ability to perceive subtle differences by learning the correlations between local area features at a fine-grained level, helping to maintain the consistency between the local area features and the global expression, and making the model more adaptable to complex lighting conditions. Meanwhile, we adopt an adaptive learning method to effectively eliminate the interference of noise and occlusion by dynamically adjusting the parameters of the Transformer Encoder’s self-attention weight matrix. Experiments demonstrate the accuracy of our CoT_AdaViT model in the Oulu-CASIA dataset as (NIR: 87.94%, VL: strong: 89.47%, weak: 84.76%, dark: 82.28%). As well as, CK+, RAF-DB, and FERPlus datasets achieved 99.20%, 91.07%, and 90.57% recognition results, which achieved excellent performance and verified that the model has strong recognition accuracy and robustness in complex scenes.

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Section: Comparision Studymentioning

confidence: 99%

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Xiong,

Zhang,

Zheng

et al. 2024

Applied Sciences

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“…The research study [16] focuses on the detection of driver drowsiness by analyzing facial features and landmarks. This experiment utilizes two public datasets, in addition to creating a specific video-based VBDDD (Video-Based Driver Drowsiness Detection) dataset.…”

Section: Literature Analysismentioning

confidence: 99%

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

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Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver's reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness using physiological signals, which often require direct contact with the driver's body. This can be uncomfortable and distracting. This study aimed at detecting driver drowsiness through eye movement behavior imagery of the driver. We utilized a standard image dataset based on the eye movement behavior of drivers to conduct this research experiment. We proposed a novel transfer learning-based features generation which combined the strengths of the Visual Geometry Group (VGG-16) and Light Gradient-Boosting Machine (LGBM) methods. The proposed VGLG (VGG16-LGBM) approach first extracts spatial features from input eye image data and then generates salient transfer features using LGBM. Experimental evaluations reveal that the k-neighbors classifier outperformed the state-of-the-art approach with a high-performance accuracy of 99%. The computational complexity analysis shows that the proposed approach detects driver drowsiness in 0.00829 seconds. We have enhanced the performance through hyperparameter tuning and validations using k-fold validation. This research has the potential to revolutionize driver drowsiness detection, aiming to prevent road accidents and save precious lives.

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“…Therefore, the classification accuracy of these methods is difficult to further improve. How to learn features with high discrimination and further improve classification accuracy is a very important challenge currently faced [15]. In addition, the computational complexity of deep learning models is relatively high, requiring a large amount of training data [16].…”

Section: Introductionmentioning

confidence: 99%

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Zhang,

Yang,

2024

Vehicles

Self Cite

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Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.

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A Robust Driver Emotion Recognition Method Based on High-Purity Feature Separation

Cited by 16 publications

References 49 publications

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

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