Automatic Depression Detection Based on Merged Convolutional Neural Networks Using facial features

Authors: Niranjan L. Bhale, Pranjal S. Dhage, Sakshi A. Chaudhari, Atharv S. Mondhe, Rahul S. Kshirsagar

Country: India

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Abstract: Mental health is a vital component of overall well-being, significantly influencing personal and professional success. Common issues such as stress, anxiety, and depression can impede daily functioning, strain relationships, and reduce productivity. As the global prevalence of mental health challenges increases, early identification and intervention are crucial. This project proposes the development of an automated system for detecting signs of depression through the analysis of facial features captured in frontal face videos. By leveraging advanced image processing and machine learning techniques, the system aims to accurately assess emotional states, enabling timely support for individuals who may be unaware of their condition or reluctant to disclose their struggles. Ultimately, this innovative approach seeks to enhance mental health awareness and promote proactive interventions in diverse populations.

Keywords: -

Paper Id: 231547

Published On: 2024-11-09

Published In: Volume 12, Issue 6, November-December 2024

Cite This: Automatic Depression Detection Based on Merged Convolutional Neural Networks Using facial features - Niranjan L. Bhale, Pranjal S. Dhage, Sakshi A. Chaudhari, Atharv S. Mondhe, Rahul S. Kshirsagar - IJIRMPS Volume 12, Issue 6, November-December 2024.

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