This Python code leverages InceptionV3 for feature extraction and Haar cascades for face detection in real-time webcam video. It highlights detected faces with green rectangles, providing an interactive face recognition system that responds to user input, making it engaging and visually appealing.
To set up your development environment and install the required dependencies, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/himanshu-commits/Face-Detection.git cd Face-Detection -
Install the necessary Python packages:
pip install numpy pip install pandas pip install matplotlib pip install seaborn pip install keras pip install tensorflow pip install opencv-python pip install pillow pip install requests pip install scikit-learn
To collect images for face recognition using OpenCV, run the following script:
python FaceSeamlessnetry.ipynbThis script utilizes OpenCV to capture frames from the webcam, detects faces, and saves cropped faces to a specified directory.
To run the face recognition system, execute the following script:
python face_frontend_model.ipynbThe script uses a pre-trained face recognition model to predict whether a face is present in the webcam feed.
If you want to retrain the face recognition model, use the following command:
python face_recognition_model.ipynbThis script implements transfer learning with a VGG16 model on your dataset. The trained model is saved for future use.
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