- Python 3.4+
- OpenCV 3.1.0
- Numpy
- Ip Camera (I'm using the android app "IP Webcam")
pip install opencv-contrib-python
This project consist of 3 parts, which are:
- Creating datasets (face_datasets.py)
- Train the model (training.py)
- Face Recognition (face_recognition.py)
- Please make sure that you have folders called 'dataset' and 'trainer' in the same directory
- Run in the command line the face_datasets.py for taking your face image as datasets. Don't forget to set each person's face to unique ID (You need to edit the code everytime, or maybe just change the id variable to raw_input[OPTIONAL])
- If you have more face to be include, change the ID and run the program again
- Train your datasets by running training.py
- Lastly, run face_recognition.py
- Object Detection using Haar-like features, link: https://www.cs.utexas.edu/~grauman/courses/spring2008/slides/Faces_demo.pdf
- Face Detection using Haar Cascades, link: https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html
- Haar-like Features, link: https://en.wikipedia.org/wiki/Haar-like_features
- Object Detection using Haar-cascades Classifier, link: https://ds.cs.ut.ee/Members/artjom85/2014dss-course-media/Object%20detection%20using%20Haar-final.pdf
- OpenCV Documentation, link: https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorials.html
- If you don't understand what the code do, I already comments it line by line.
- You also can Google the syntax, and read the explanation from OpenCV Documentation.
- Feel free to ask me through my email: [email protected]