Abstract

The assessment of emotional states via facial expressions has considerable ramifications for the identification and treatment of mental health disorders, including depression. Utilizing sophisticated machine learning models can improve the precision and sensitivity of identifying intricate emotional states from visual data. This research presents the residual-liquid-support-vector network (ReLiS-Net) model, which integrates residual neural network (ResNet), liquid neural network (LNN) and support vector machine (SVM) to effectively capture spatial and temporal variations in facial expressions. Using the extended Cohn–Kanade (CK+) dataset, the proposed model ReLis-Net exhibits exceptional performance, achieving 99.34% accuracy, 99.12% sensitivity, 98.92% specificity and a 99.29% F1-score. The robustness of ReLiS-Net is confirmed in comparison to modern models, demonstrating its greater capacity to analyze emotional nuances essential for detecting depressive states. This improved analytical approach not only delivers superior predictive performance but also yields insights into emotional tendencies that may signify underlying mental health concerns, thereby facilitating early intervention measures.

Keywords

Facial expression recognition, Depression detection, Residual-liquid-support-vector network (ReLiS-Net), Deep learning and hybrid models, Mental health analytics.

Cite this article

Sahoo B, Gupta A. ReLiS-Net for emotion recognition: an AI-driven approach to advancing mental health diagnostics. International Journal of Advanced Technology and Engineering Exploration. 2026;13(135):263-279. DOI : 10.19101/IJATEE.2024.111102087

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