An open CV-based approach to mood regulation

Yang Yu; Xiaoge Tang; Fei Li; Peichen Song; Jiahao Shen; Changbin Zhu; Yuyun Fu; Hongjun Li

doi:10.1117/12.3033043

5 July 2024 An open CV-based approach to mood regulation

Yang Yu, Xiaoge Tang, Fei Li, Peichen Song, Jiahao Shen, Changbin Zhu, Yuyun Fu, Hongjun Li

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842U (2024) https://doi.org/10.1117/12.3033043
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In recent years, due to the influence of the post-epidemic era, some young people have mild anxiety, emotional depression and other problems^[1]. Many experts and scholars at home and abroad have proposed multi-modal emotion analysis methods. Based on this, we propose an OpencV-based emotion regulation method. We use Python language as a programming language, based on the related technologies of OpenCV module, PyQt UI interface technology, face recognition and speech recognition technology, and the use of multi-threading technology in the entire program, to avoid the occurrence of program no response phenomenon, to ensure the smooth operation of the program to the greatest extent. The facial expression data is collected through the camera, and the audio data is collected through the microphone and uploaded to the program. When the bad mood is detected, the user's mood is adjusted through language comfort, appropriate music, appropriate lighting and other ways to maintain the user's emotional stability. Or detect that the user is currently happy, but also through music and other means to enhance the user's interest in life. The product contains core devices such as cameras, microphones, and Raspberry PI development boards, so that the current flows in each component along the expected route to complete the modulation, demodulation and coding functions. The method proposed in this paper can effectively improve the emotion recognition accuracy, and the accuracy rate reaches 87.49% in the multi-class emotion recognition tasks in the open data set, which has a stable recognition effect

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Yu, Xiaoge Tang, Fei Li, Peichen Song, Jiahao Shen, Changbin Zhu, Yuyun Fu, and Hongjun Li "An open CV-based approach to mood regulation", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842U (5 July 2024); https://doi.org/10.1117/12.3033043

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KEYWORDS

Emotion

Facial recognition systems

Speech recognition

Education and training

Cameras

Data modeling

Image processing

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