PROJECTS

Artificial Intelligent System for Automatic Depression Level Analysis

            Depression is the most prevalent mood disorder worldwide having a significant impact on well-being and functionality, and important 
            personal, family and societaleffects. The early and accurate detection of signs related to depression could havemany benefits for 
            both clinicians and affected individuals.The review outlines methods and algorithms for visual feature extraction, 
            dimensionality reduction, decision methods for classification and regressionapproaches, as well as different fusion strategies. 
            A quantitative meta-analysis ofreported results, relying on performance metrics robust to chance, is included, identifying general 
            trends and key unresolved issues to be considered in future studies ofautomatic depression assessment utilizing visual cues alone or 
            in combination withvisual cues. The proposed work also carried out to predict the depression level according to current input of face 
            images using deep learning.