ML-Driven Real Time Emotion and Stress Level Monitoring
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ML-Driven Real Time Emotion and Stress Level Monitoring

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Total downloads: 31

This project uses a facial expression recognition (FER) model to detect emotions and estimate stress levels in real time. It works by tracking faces, extracting facial features, and classifying emotions using machine learning. The system then analyzes these emotions to assess potential stress levels.

The model is built in Python and works on both Windows and Linux, though it has mainly been tested on Windows. It uses OpenCV for real-time face detection and integrates deep learning models like CNN for accurate emotion classification. The system is designed to process data quickly, making it useful for applications like mental health monitoring, human-computer interaction, and security surveillance.

Note: This project is being developed and tested exclusively on Google Colab.

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Description

Experience the future of emotion and stress analysis with our Facial Expression Detection System! Powered by cutting-edge machine learning and computer vision, simply upload an image from your device or capture a live image, hit ‘Analyze,’ and receive instant insights into emotions and stress levels. Revolutionize mental health tracking and emotion recognition with just a click!

Need Help With This Code?

Work with a PhD research mentor to adapt this code to your data, extend it for your thesis, or turn the results into a publishable paper.

Mentors who publish in this area
Image Processing/ OpenCV mentors Computer Vision and Natural language Processing mentors Machine Learning and Deep Learning mentors Machine Learning/Deep Learning mentors
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Bharti_Parate

7 reviews for ML-Driven Real Time Emotion and Stress Level Monitoring

  1. shubh.shrivastava –

    Nice

  2. md.ridwan –

    Good

  3. md.ridwan –

    Good

  4. kapil.gupta –

    ok

  5. majid.ali –

    Thank you

  6. omar.faruq (verified owner) –

    jiyesttt

  7. omar.faruq (verified owner) –

    buy

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