International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Depression, affecting over 280 million individuals globally, imposes an economic burden exceeding $1 trillion annually through reduced productivity and healthcare costs. This paper presents an innovative hybrid system integrating natural language processing (NLP) and facial recognition to identify early depressive symptoms in students, utilizing the Reddit Self-reported Depression Diagnosis (RSDD) dataset and ethically sourced classroom imagery. Combining textual analysis (TF-IDF, BERT embeddings) with facial feature extraction (HOG, PCA, FaceNet), the system achieves 0.92 accuracy, 0.90 F1-score, and 0.94 AUC, surpassing NLP-only (0.90 accuracy, 0.88 F1-score, 0.91 AUC) and facial recognition-only (0.85 accuracy, 0.83 F1-score, 0.87 AUC) baselines. In a case study with 500 students, it identified 87% of at-risk individuals, demonstrating practical utility. The methodology employs robust preprocessing, feature fusion, and real-time processing tailored for educational settings, enabling efficient monitoring and intervention. Ethical safeguards, including differential privacy, data anonymization, and informed consent, address privacy concerns and mitigate biases in Reddit’s predominantly young, male demographic. Designed for scalability, it supports mental health interventions and attendance tracking, offering a cost-effective solution to promote student well-being. By integrating advanced machine learning with ethical frameworks, the system aligns with global mental health strategies, reducing the burden of undiagnosed depression. Its modular design enables adaptation to diverse educational contexts, highlighting the potential of multimodal approaches for complex mental health challenges.

Keywords: Depression Detection, NLP, Facial Recognition, Deep Learning, BERT, FaceNet, Mental Health, Ethics.

Downloads: | DOI: 10.17148/IJARCCE.2025.14831