Facial-Emotion-Recognition-DeepFace-StreamLit

• 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. The emotion labels are displayed on the frames in real-time.
• to implement realtime emotion monitoring.
• Created a streamLit application for the facial emotion recognition of human faces.
• Give this repository a ⭐ if you liked it, since it took me time to understand and implement this
• Made with ❤️ by Shrimanta Satpati

Dependencies

• deepface: A deep learning facial analysis library that provides pre-trained models for facial emotion detection. It relies on TensorFlow for the underlying deep learning operations.
• OpenCV: An open-source computer vision library used for image and video processing.

Usage

Initial steps:

• Git clone this repository Run: git clone https://github.com/shrimantasatpati/Facial-Emotion-Recognition-DeepFace-StreamLit.git
• Run: Facial-Emotion-Recognition-DeepFace-StreamLit

1. Install the required dependencies:
   • You can use pip install -r requirements.txt
   • Or you can install dependencies individually:
     • pip install deepface
     • pip install opencv-python
2. Run the code:
   • Execute the Python script.
   • The webcam will open, and real-time facial emotion detection will start.
   • Emotion labels will be displayed on the frames around detected faces. (Using the DeepFace extended models to predict age, emotions, gender and racial identity of the persons.)

StreamLit

• Local Deployment on StreamLit framework.