This project is a program that uses a webcam to detect faces in real-time. It utilizes the Dlib library for face detection and extracts features from recognized faces.
- Real-time face detection
- Multiple face recognition
- Landmark extraction from faces
- Python 3.x
- Dlib
- OpenCV
- NumPy
Lowering the webcam resolution can reduce the number of pixels to process, thus improving speed. For example, set it to 640x480 resolution.
Instead of processing every frame, you can skip frames at regular intervals. For example, perform face recognition every 2-3 frames.
You can use faster face detection and recognition algorithms. For instance, you might use OpenCV's Haar Cascade instead of Dlib.
Run frame capture and face recognition in separate threads to perform both tasks in parallel. You can utilize Python's threading or multiprocessing modules.
If possible, leverage the GPU to enhance computation speed. Libraries like TensorFlow and PyTorch support GPU acceleration.
Store results for already recognized faces, and skip the recognition process when the same face appears again.
Improve performance by reducing unnecessary computations or optimizing frequently called functions. For example, change the code so that transformed images are only computed when needed, rather than every time.