The Application and Analysis of Emotion Recognition Based on Modern Technology
This article provides a comprehensive analysis of various emotion recognition methods, focusing on speech emotion recognition, facial expression recognition, and physiological signal emotion recognition. The primary aim is to evaluate the advantages and disadvantages of these methods, offering insig...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03012.pdf |
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| Summary: | This article provides a comprehensive analysis of various emotion recognition methods, focusing on speech emotion recognition, facial expression recognition, and physiological signal emotion recognition. The primary aim is to evaluate the advantages and disadvantages of these methods, offering insights into selecting the most appropriate approach for different application scenarios. The study involves collecting and analysing experimental data, exploring their respective strengths and limitations, and proposing potential solutions to enhance their effectiveness. Speech emotion recognition is effective but sensitive to noise and speaker variability, while facial expression recognition excels under controlled conditions but struggles with changes in lighting and angles. Physiological signal recognition offers deep insights into internal emotional states but requires complex signal processing and is vulnerable to external interferences. Despite the growing application of emotion recognition technology across various fields, including healthcare, traffic safety, and security, there remain significant challenges related to accuracy, robustness, and privacy. This study highlights the need for continued research to improve these technologies, particularly in enhancing their robustness and adaptability. The findings provide valuable guidance for researchers and practitioners seeking to optimize emotion recognition systems for diverse real-world applications. |
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| ISSN: | 2271-2097 |