Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Tang, Chaolin; Zhang, Dong; Tian, Qichuan

doi:10.3390/app132212418

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…This enables the model to leverage the potential benefits of transferable cross-domain local features, resulting in improved performance in facial emotion recognition tasks. Tang et al [12] introduced BGA-Net, a novel approach to facial expression recognition. BGA-Net integrates bidirectional gated recurrent units (BiGRUs), a convolutional neural network (CNN), and an attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

Yeh,

Lin,

Chang

et al. 2023

Applied Sciences

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Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system.

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Section: Related Workmentioning

confidence: 99%

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

Yeh,

Lin,

Chang

et al. 2023

Applied Sciences

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A Personalized Multi-region Perception Network for Learner Facial Expression Recognition in Online Learning

Xiong,

Zhou,

Wang

et al. 2024

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Track

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Empathetic Deep Learning: Transferring Adult Speech Emotion Models to Children With Gender-Specific Adaptations Using Neural Embeddings

Lesyk,

Arias-Vergara,

Nöth

et al. 2024

Hum-Cent Intell Syst

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Understanding and recognizing emotional states through speech has vast implications in areas ranging from customer service to mental health. In this paper, we investigate the relationship between adults and children for the task of automatic speech emotion recognition, focusing on the critical issue of limited datasets for children’s emotions. We use two databases: IEMOCAP, which contains emotional speech recordings from adults, and AIBO, which includes recordings from children. To address the dataset limitations, we employ transfer learning by training a neural network to classify adult emotional speech using a Wav2Vec model for feature extraction, followed by a classification head for the downstream task. However, the labels between IEMOCAP and AIBO do not align perfectly, presenting a challenge in emotional mapping. To tackle this, we perform inference on children’s data to examine how emotional labels in IEMOCAP correspond to those in AIBO, highlighting the complexities of cross-age emotional transfer. This approach achieved F-scores of up to 0.47. In addition, we trained male and female IEMOCAP models to determine how variations in gender within adult speech affect emotional mapping in children data. Some of our findings indicate that female samples align more with high arousal emotions, while male samples align more with low arousal emotion, underscoring the importance of gender in emotion recognition. To the best of our knowledge, this is the first study in the field of deep learning applications on emotion recognition that analyses the effects of genders and age groups on emotional mapping.

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Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Cited by 4 publications

References 43 publications

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

A Personalized Multi-region Perception Network for Learner Facial Expression Recognition in Online Learning

Empathetic Deep Learning: Transferring Adult Speech Emotion Models to Children With Gender-Specific Adaptations Using Neural Embeddings

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