Real-Time Depression Detection via Resnet-50 Facial Emotion Recognition
DOI:
https://doi.org/10.15662/IJEETR.2026.0802222Keywords:
Real-Time Depression Detection, Facial Emotion Recognition, Deep Learning, ResNet-50, Artificial Intelligence, Explainable AI, Mental Health Monitoring, Computer Vision, Emotional State Classification, Smart Healthcare SystemsAbstract
The increasing prevalence of mental health disorders, particularly depression, necessitates the development of intelligent and automated detection systems to enable early diagnosis and timely intervention. Traditional depression assessment methods, which rely primarily on self-reported questionnaires and manual psychological evaluations, often lack objectivity and fail to capture subtle emotional variations in real-time environments. In response, Artificial Intelligence (AI) has emerged as a transformative solution for enhancing mental health monitoring systems. AI-powered depression detection frameworks leverage machine learning (ML) and deep learning (DL) techniques to analyze facial expressions and behavioral patterns, identifying emotional cues indicative of depressive states.
Recent advancements in deep learning have enabled the development of adaptive models capable of learning complex facial features with high precision. Convolutional Neural Networks (CNNs), particularly ResNet-50, have been employed to extract deep hierarchical representations from facial image data, improving classification accuracy and robustness. Additionally, temporal learning mechanisms can be integrated to analyze emotional trends over time, enhancing real- time depression assessment. Data augmentation and optimized training strategies further contribute to improved detection performance in diverse real-world conditions
The integration of Explainable AI (XAI) techniques into depression detection frameworks enhances system transparency, allowing healthcare professionals to interpret and trust AI-driven decisions. Moreover, the application of AI-based facial emotion recognition systems in smart healthcare and remote monitoring environments demonstrates the feasibility of deploying intelligent mental health assessment tools in practical scenarios.
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