https://github.com/alejandromeyer/facial-recognition
This project uses DeepFace and OpenCV to detect facial emotions in real-time via webcam. It processes video frames, identifies faces, and classifies emotions dynamically. This is an exploratory implementation to test DeepFace’s capabilities.
https://github.com/alejandromeyer/facial-recognition
deepface face-recognition opencv python tensorflow
Last synced: about 2 months ago
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This project uses DeepFace and OpenCV to detect facial emotions in real-time via webcam. It processes video frames, identifies faces, and classifies emotions dynamically. This is an exploratory implementation to test DeepFace’s capabilities.
- Host: GitHub
- URL: https://github.com/alejandromeyer/facial-recognition
- Owner: AlejandroMeyer
- Created: 2025-03-02T13:13:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-02T13:19:33.000Z (over 1 year ago)
- Last Synced: 2025-05-31T00:28:42.647Z (about 1 year ago)
- Topics: deepface, face-recognition, opencv, python, tensorflow
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Real-Time Emotion Detection with DeepFace
This project explores the capabilities of DeepFace, a powerful Python library for deep learning-based facial recognition and analysis. The script utilizes DeepFace and OpenCV to detect emotions in real-time using a webcam.
## 🚀 About the Project
The goal of this project is to experiment with DeepFace's emotion detection capabilities. The current implementation detects a single face in the video stream and classifies its dominant emotion as happy, angry, sad, surprised, or neutral (thoughtful).
This project is still in an exploratory phase, and future improvements could include:
- Enhancing accuracy with different deep learning models.
- Recognizing multiple faces simultaneously.
- Optimizing performance for real-time applications.
## 🛠️ Technologies Used
- Python
- OpenCV
- DeepFace
- TensorFlow
## 📌 How It Works
1. The script captures video from the webcam.
2. It processes each frame using DeepFace’s `analyze` function to detect emotions.
3. A bounding box is drawn around the detected face with a label indicating the predicted emotion.
4. The user can exit by pressing 'q'.
## 📦 Installation
Before running the script, ensure you have the required dependencies installed in the file: requirements.txt
1. Clone the repository:
```bash
[git clone https://github.com/AlejandroMeyer/Facial-Recognition]
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Feel free to contribute or report any issues!