Depression Detection using Machine Learning
In recent years, Integration of machine learning (ML) and Artificial Intelligence (AI) techniques have given potential in various domains, including medical health. This project is about a novel or different approach “Artificial Intelligence based Facial Emotions Analysis for Depression Detection”, focused about deep learning and artificial intelligence to detect and identify the depression levels based on facial emotion analysis. This project based on combination of python programming language and Convolutional Neural Network (CNN) model for facial emotion recognition. The main objective of this model is to recognize the emotions of person- Depressed or Non-Depressed- by facial expression analyzing from videos. These emotions indicates a person mental health state. for achieving high accuracy in emotion recognition and depression classification, the AlexNet CNN model is used.
Project Functionality of Depression Detection System using Facial Expression:-
Depression Detection System using Machine Learning, this project developed using python programming language and tkinter (python library) is used for developing graphical user interface. In this system user have to upload input video and model first it will detect and classify it as depressed and non-depressed person from facial emotion recognition.
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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