Abstract: Depression is the most common mood disease in the world, with serious consequences for one's well-being and functionality, as well as substantial personal, familial, and societal consequences. Early and effective diagnosis of depression symptoms could provide numerous advantages for both physicians and those who are affected. The goal of this study was to create and evaluate a methodology that could detect visual symptoms of depression and help clinicians make judgments. The field of automatic depression evaluation based on visual clues is fast expanding. The current comprehensive assessment of existing methodologies focuses on image processing and machine learning algorithms, as documented in over sixty articles over the last ten years. The visual signs of depression, various data collection methodologies, and available datasets are all summarized. The review discusses visual feature extraction methods and algorithms, dimensionality reduction, classification and regression decision methods, and various fusion methodologies. A quantitative meta-analysis of published data is given, based on performance criteria that are robust to chance, to indicate general trends and important unresolved concerns for future investigations of automatic depression evaluation using visual cues alone or in combination with visual cues. The proposed work also used deep learning to forecast the level of depression based on current input of face photos.
Keywords: Convolutional Neural Network, Deep Learning, Dataset, Depression.
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DOI:
10.17148/IJARCCE.2022.11692
