International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Depression Detection System Using Facial Recognition Using Python

Author(s) Suryansh Desai, Pranav Sawant, Mayank Raut, Anish Bandal, Yogesh Gaikwad
Country India
Abstract This paper presents a comprehensive mental health assessment toolkit implemented in Python, consisting of two distinct modules. The first module focuses on depression detection through a quiz, while the second module performs emotion detection using libraries such as DeepFace and OpenCV. The Depression Detection Module aims to provide a self-assessment tool for individuals who suspect they may be experiencing symptoms of depression. The emotion detection module uses advanced computer vision techniques and the DeepFace library to analyze facial expressions and recognize emotional states in real time via a webcam or recorded images. OpenCV is used to capture and process images or video streams, while DeepFace's deep learning models accurately classify emotions, including happiness, sadness, anger, fear, surprise, and neutrality. While the system shows promise in contributing to the field of mental health screening, it is essential to address ethical considerations related to user privacy and consent. Striking a balance between technological advancements and ethical guidelines ensures the responsible and effective deployment of such tools.
Keywords Depression Detection, Python, Deepface library, OpenCV, Quiz, Realtime.
Field Computer > Data / Information
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-20
Cite This Depression Detection System Using Facial Recognition Using Python - Suryansh Desai, Pranav Sawant, Mayank Raut, Anish Bandal, Yogesh Gaikwad - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19652
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19652
Short DOI https://doi.org/gtvt3z
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