FERNET: An Integrated Hybrid DCNN Model for Driver Stress Monitoring via Facial Expressions

Gupta, Chinmay; Kumar, Mohit; Yadav, Arun Kumar; Yadav, Divakar

doi:10.1142/s0218001423570021

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Traditional methods for stress assessment mainly rely on non-invasive approaches such as speech and facial expression analysis. Gupta et al [ 4 ] proposed a driver stress detection method by analyzing facial expressions to monitor stress levels based on a hybrid deep convolutional neural network model. Meanwhile, Chu et al [ 5 ] designed an automatic speech analysis program to evaluate stress burden and psychological health.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

Dong,

Xu,

Zheng

et al. 2024

Brain Sciences

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This paper proposes a new hybrid model for classifying stress states using EEG signals, combining multi-domain transfer entropy (TrEn) with a two-dimensional PCANet (2D-PCANet) approach. The aim is to create an automated system for identifying stress levels, which is crucial for early intervention and mental health management. A major challenge in this field lies in extracting meaningful emotional information from the complex patterns observed in EEG. Our model addresses this by initially applying independent component analysis (ICA) to purify the EEG signals, enhancing the clarity for further analysis. We then leverage the adaptability of the fractional Fourier transform (FrFT) to represent the EEG data in time, frequency, and time–frequency domains. This multi-domain representation allows for a more nuanced understanding of the brain’s activity in response to stress. The subsequent stage involves the deployment of a two-layer 2D-PCANet network designed to autonomously distill EEG features associated with stress. These features are then classified by a support vector machine (SVM) to determine the stress state. Moreover, stress induction and data acquisition experiments are designed. We employed two distinct tasks known to trigger stress responses. Other stress-inducing elements that enhance the stress response were included in the experimental design, such as time limits and performance feedback. The EEG data collected from 15 participants were retained. The proposed algorithm achieves an average accuracy of over 92% on this self-collected dataset, enabling stress state detection under different task-induced conditions.

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Section: Introductionmentioning

confidence: 99%

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

Dong,

Xu,

Zheng

et al. 2024

Brain Sciences

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Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

Chen,

Liu,

et al. 2024

Journal of Loss Prevention in the Process Industries

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Facial Emotion Recognition Combining Auxiliary Classifiers and Multiscale CBAM Attention Mechanisms

Shang,

Yan,

Liu

et al. 2024

IEEE Access

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Facial emotions are the most intuitive way to react to changes in inner emotions. We propose a facial emotion recognition method that combines auxiliary classifiers(Acs) and multi-scale CBAM(MCBAM) by improving the Xception network model. And we design a lightweight network model AMDCNN. We introduce Acs in the middle layers of the model. The features extracted from the middle layer portion of the model are utilized to aid in emotion recognition. Finally, the recognition results of the Acs and the main classifier are adaptively weighted and fused to obtain better emotion recognition results. This enables better utilization of the feature information extracted from the intermediate layers and further reduces the feature loss caused by the downsampling process of the convolutional layer. The CBAM does not increase the number of parameters and computation too much, and it enables the model to better focus on the important areas of the face. We apply it to the proposed lightweight model and improve it further. The width is increased by introducing a multi-branch convolutional structure and utilizing convolutional layers with different kernel sizes. This allows for more adequate spatial and channel features during feature extraction, allowing the model to more accurately focus on important facial regions. Our proposed model was experimentally validated on datasets of FER2013, FERPlus, RAF-DB and CK+, with accuracies of 69.82%, 85.40%, 86.77% and 99.49%, respectively. The number of parameters of the proposed model is only 1.6M. Our model is a good competitive advantage compared with other lightweight models.

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FERNET: An Integrated Hybrid DCNN Model for Driver Stress Monitoring via Facial Expressions

Cited by 4 publications

References 24 publications

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

Facial Emotion Recognition Combining Auxiliary Classifiers and Multiscale CBAM Attention Mechanisms

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