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      • One of the groundbreaking applications of this technology is live emotion recognition using the DeepFace library. By harnessing the capabilities of DeepFace, we can process live images and extract valuable facial analysis insights.
      medium.com/@harshithaparitala1/transforming-facial-expressions-into-insights-real-time-emotion-recognition-using-deepface-2b91a03f277d

      Transforming Facial Expressions into Insights: Real-Time ...

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  3. github.com › ajitharunai › Facial-EmotionReal-time Facial Emotion Recognition using OpenCV and Deepface

    This project demonstrates the implementation of real-time facial emotion recognition using the deepface library and OpenCV. The objective is to capture live video from a webcam, identify faces within the video stream, and predict the corresponding emotions for each detected face.

  4. github.com › serengil › deepfaceGitHub - serengil/deepface: A Lightweight Face Recognition ...

    • Overview
    • Installation
    • Contribution
    • Support
    • Citation
    • Licence

    Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace.

    Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

    See full list on github.com

    The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well.

    Secondly, DeepFace is also available at Conda. You can alternatively install the package via conda.

    Thirdly, you can install deepface from its source code.

    Then you will be able to import the library and use its functionalities.

    Facial Recognition - Demo

    A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

    See full list on github.com

    Pull requests are more than welcome! You should run the unit tests and linting locally by running make test && make lint before creating a PR. Once a PR sent, GitHub test workflow will be run automatically and unit test results will be available in GitHub actions before approval. Besides, workflow will evaluate the code with pylint as well.

    See full list on github.com

    There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

    You can also support this work on Patreon or GitHub Sponsors.

    See full list on github.com

    Please cite deepface in your publications if it helps your research - see CITATIONS for more details. Here are its BibTex entries:

    If you use deepface in your research for facial recogntion purposes, please cite this publication.

    If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction or face detection purposes, please cite this publication.

    Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

    See full list on github.com

    DeepFace is licensed under the MIT License - see LICENSE for more details.

    DeepFace wraps some external face recognition models: VGG-Face, Facenet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, and SFace. Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.

    See full list on github.com

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  6. fuyofulo.medium.com › real-time-facial-emotionReal-time Facial Emotion Recognition using Deep ... - Medium

    Nov 15, 2023 · In this article, we are going to leverage the power of deep learning and OpenCV to dive into real-time facial emotion recognition from unraveling the complexities of the building a...

  7. github.com › shrimantasatpati › Facial-EmotionFacial-Emotion-Recognition-DeepFace-StreamLit - GitHub

    This project implements real-time facial emotion detection using the deepface library and OpenCV. It captures video from the webcam, detects faces, and predicts the emotions associated with each face.

  8. medium.com › @adarshkumarchaurasiya › recognitionRecognition Model Real Time-Facial Emotion ... - Medium

    Mar 20, 2024 · One of the popular libraries for this task is DeepFace, which provides pre-trained deep learning models for facial analysis tasks, including emotion recognition. Here’s an introduction to...

  9. www.linkedin.com › pulse › facial-emotionFacial Emotion Recognition with OpenCV and Deepface: Step-by ...

    Aug 10, 2023 · Real-time Facial Emotion Recognition using OpenCV and Deepface. This project demonstrates the implementation of real-time facial emotion recognition using the `deepface` library and...

  10. ieeexplore.ieee.org › document › 10397747Live Emotion Detection Using Deepface | IEEE Conference ...

    Sep 16, 2023 · The goal is to use computer vision and machine learning techniques to automatically detect facial expressions and identify emotions in real-time. The Deepface package is used as the main tool in this investigation because of its capability for facial matching and emotion recognition.