Facial recognition and analysis: A machine learning-based pathway to corporate mental health management
Background In modern workplaces, emotional well-being significantly impacts productivity, interpersonal relationships, and organizational stability. This study introduced an innovative facial-based emotion recognition system aimed at the real-time monitoring and management of employee emotional stat...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251335542 |
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| author | Zicheng Zhang Tianshu Zhang Jie Yang |
| author_facet | Zicheng Zhang Tianshu Zhang Jie Yang |
| author_sort | Zicheng Zhang |
| collection | DOAJ |
| description | Background In modern workplaces, emotional well-being significantly impacts productivity, interpersonal relationships, and organizational stability. This study introduced an innovative facial-based emotion recognition system aimed at the real-time monitoring and management of employee emotional states. Methods Utilizing the RetinaFace model for facial detection, the Dlib algorithm for feature extraction, and VGG16 for micro-expression classification, the system constructed a 10-dimensional emotion feature vector. Emotional anomalies were identified using the K-Nearest Neighbors algorithm and assessed with a 3σ-based risk evaluation method. Results The system achieved high accuracy in emotion classification, as demonstrated by an empirical analysis, where VGG16 outperformed MobileNet and ResNet50 in key metrics such as accuracy, precision, and recall. Data augmentation techniques were employed to enhance the performance of the micro-expression classification model. Conclusion These techniques improved the across diverse emotional expressions, resulting in more accurate and robust emotion recognition. When deployed in a corporate environment, the system successfully monitored employees’ emotional trends, identified potential risks, and provided actionable insights into early intervention. This study contributes to advancing corporate mental health management and lays the foundation for scalable emotion-based support systems in organizational settings. |
| format | Article |
| id | doaj-art-a2c6197fed2c43e7a7df4784fa42f4b2 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-a2c6197fed2c43e7a7df4784fa42f4b22025-08-20T03:14:19ZengSAGE PublishingDigital Health2055-20762025-04-011110.1177/20552076251335542Facial recognition and analysis: A machine learning-based pathway to corporate mental health managementZicheng Zhang0Tianshu Zhang1Jie Yang2 Research and Development Department, Nanjing Yunshe intelligent technology Co., LTD, Nanjing, China School of Information Management, , Nanjing, China Research and Development Department, Nanjing Yunshe intelligent technology Co., LTD, Nanjing, ChinaBackground In modern workplaces, emotional well-being significantly impacts productivity, interpersonal relationships, and organizational stability. This study introduced an innovative facial-based emotion recognition system aimed at the real-time monitoring and management of employee emotional states. Methods Utilizing the RetinaFace model for facial detection, the Dlib algorithm for feature extraction, and VGG16 for micro-expression classification, the system constructed a 10-dimensional emotion feature vector. Emotional anomalies were identified using the K-Nearest Neighbors algorithm and assessed with a 3σ-based risk evaluation method. Results The system achieved high accuracy in emotion classification, as demonstrated by an empirical analysis, where VGG16 outperformed MobileNet and ResNet50 in key metrics such as accuracy, precision, and recall. Data augmentation techniques were employed to enhance the performance of the micro-expression classification model. Conclusion These techniques improved the across diverse emotional expressions, resulting in more accurate and robust emotion recognition. When deployed in a corporate environment, the system successfully monitored employees’ emotional trends, identified potential risks, and provided actionable insights into early intervention. This study contributes to advancing corporate mental health management and lays the foundation for scalable emotion-based support systems in organizational settings.https://doi.org/10.1177/20552076251335542 |
| spellingShingle | Zicheng Zhang Tianshu Zhang Jie Yang Facial recognition and analysis: A machine learning-based pathway to corporate mental health management Digital Health |
| title | Facial recognition and analysis: A machine learning-based pathway to corporate mental health management |
| title_full | Facial recognition and analysis: A machine learning-based pathway to corporate mental health management |
| title_fullStr | Facial recognition and analysis: A machine learning-based pathway to corporate mental health management |
| title_full_unstemmed | Facial recognition and analysis: A machine learning-based pathway to corporate mental health management |
| title_short | Facial recognition and analysis: A machine learning-based pathway to corporate mental health management |
| title_sort | facial recognition and analysis a machine learning based pathway to corporate mental health management |
| url | https://doi.org/10.1177/20552076251335542 |
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