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Real-Time Depression Detection using Emotion Recognition Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 66 |
| Year of Publication: 2025 |
| Authors: Sonali Singh, Navita Srivastava |
10.5120/ijca2025924467
|
Sonali Singh, Navita Srivastava . Real-Time Depression Detection using Emotion Recognition Techniques. International Journal of Computer Applications. 186, 66 ( Feb 2025), 40-48. DOI=10.5120/ijca2025924467
Abstract
When it comes to health, mental health is a very widespread problem today. health, where nearly 25% of the world's population suffers from some mental illness. Artificial Intelligence (AI) is a concept that has evolved considerably and is expected to soon bring improvements and help to people in various fields. The mental health field is not excluded, so artificial intelligence can help in the performance of health services, whether by medical staff or patients. Within mental health, it has been observed that by recognizing facial expressions, depression, schizophrenia, or other similar conditions. However, for machine learning, a large data set is required for good accuracy. In this paper, presented a facial expression recognition method using only a few training datasets, the accuracy of this training dataset was 94%. This precision is stored as the depression-detector model name.h5. The proposed model (FEDA) will facilitate the understanding of people's mental state. Human image data models have helped understand emotions and provided new application concepts in health, security, business, and education, which can be used remotely via a web application.
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