Paper Key : IRJ************250
Author: Divyesh Kachave
Date Published: 11 Nov 2024
Abstract
This research presents a novel machine learning approach for automatic depression recognition, advancing forensic psychology by accurately identifying depressive behaviors from real-world images. Unlike existing methods, our model combines spatial and temporal data to capture a wide range of depressive facial expressions and subtle behavioral cues. This architecture outperforms state-of-the-art algorithms on benchmark datasets, demonstrating robust accuracy and potential for early detection applications. By offering reliable insights into clinical depression, our method holds promise for use in forensic evaluations, mental health monitoring, and therapeutic interventions.
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