Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
Front Public Health. 2023 Dec 11;11:1323922. doi: 10.3389/fpubh.2023.1323922. eCollection 2023.
Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.
社交媒体是一种强大的沟通工具，也是我们数字环境的反映。社交媒体在 COVID-19 期间和之后起到了增强和影响的作用。许多在社交媒体上分享帖子的人实际上并不了解自己的心理健康状况。这种情况需要自动化检测精神障碍。本文提出了一种使用微观表情检测精神障碍的方法。微观表情是短暂的、无意识的面部表情，可以暗示更深层次的感受和精神状态。然而，手动检测和解释微观表情可能颇具挑战性。提出了一种基于卷积神经网络的深度学习 HybridMicroNet 模型，用于从微观表情中识别情绪。此外，还进行了一项心理健康检测的案例研究。研究结果表明，该模型在尝试根据微观表情诊断心理健康障碍时取得了很高的准确性。在 CASME 数据集上的准确率达到了 99.08%，而在 SAMM 数据集上的准确率达到了 97.62%。基于这些发现，通过分析微观表情，深度学习可能被证明是一种诊断心理健康状况的有效方法。
