A facial expression recognition algorithm incorporating SVM and explainable residual neural network

Ji, Lipeng; Wu, Shao-Ling; Gu, Xiaoqing

doi:10.1007/s11760-023-02657-1

Cited by 8 publications

(1 citation statement)

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“…Their adeptness in extracting and recognizing complex features has been well-documented [11]. These CNN architectures are powerful tools for image classification [12][13][14].…”

Section: Emotion Recognition Through Facial Expressionsmentioning

confidence: 99%

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

Roshdy,

Karar,

Kork

et al. 2024

Applied Sciences

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This paper addresses the limitations of relying solely on facial expressions for emotion recognition by proposing an advanced approach that emphasizes continuous monitoring of electroencephalography (EEG) signals. Recognizing the potential for deception in facial expressions, our study leverages the growing interest in EEG signals, tapping into advancements in deep learning and machine learning. By optimizing the configuration of EEG electrodes, our approach enhances the accuracy of emotion classification systems, offering a streamlined solution. The proposed multi-input system refines EEG-based emotion recognition efficiency and integrates facial expression analysis to enhance overall system effectiveness. Through the application of brain heat map topographies and facial expression recognition, our system, employing just nine electrodes, outperforms basic emotion recognition setups. Experimental results validate that combining facial expression analysis with EEG signals provides a more comprehensive and accurate understanding of human emotions. This innovative approach holds significance across various sectors, including healthcare, psychology, and human–computer interaction. The paper introduces a novel multi-input system approach, collaboratively fusing two powerful deep learning algorithms: two Convolutional Neural Networks (CNNs). The proposed EEG-based CNN algorithm achieves an efficiency of 87.43%, rising to 91.21% when integrated with the DeepFace CNN. The seamless integration of facial expressions and brain topographies enables the system to efficiently harness abundant information from both modalities, ensuring a thorough comprehension of human emotions. By capitalizing on the combined advantages of analyzing facial expressions and EEG-derived brain topography, this avant-garde technique substantially improves both precision and efficiency in emotion recognition systems. This enhancement establishes a foundation for the introduction of innovative applications across a spectrum of fields.

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“…Their adeptness in extracting and recognizing complex features has been well-documented [11]. These CNN architectures are powerful tools for image classification [12][13][14].…”

Section: Emotion Recognition Through Facial Expressionsmentioning

confidence: 99%