Joint Cascade Face Detection and Alignment in Python

Implementing the joint cascade face detector on AFW dataset. And this implementation is based on landmark_py. All the things have benn tested on Ubuntu 14.04.

Dependencies

---

All of the following modules can be easily installed by pip

PIL
numpy
scipy
scikit-learn
OpenCV

Install script on Ubuntu 14.04

sudo aptitude install python-pip gfortran imagemagick
sudo pip install pillow numpy scipy sklearn
sudo aptitude install python-opencv

Demo on AFW

---

I have trained a model based on AFW(only contain 337 faces) with 5 non-face images for demo. You should train the model on a big dataset

1. Download the AFW dataset here
2. Replace the location of afw by yourself in afw.txt and neg.txt in config folder(Mine is /home/samuel/data)
3. Change afw_config.py:dataPara:posList, negList by yourself

• Train on AFW

python -W ignore ./demo_train.py ../config/afw_config.py

• Detection with the Pretrained AFW Model

python -W ignore ./demo_detect.py ../config/afw_model/train.model ../config/pos.jpg

TODO

---

1. Use the non-maximum suppression to find the best face rects instead to merge the rects via cv2.grouprects

References

---

1. Face Alignment at 3000 FPS via Regressing Local Binary Features
2. Joint Cascade Face Detection and Alignment

Contact

---

If you have any questions, please email [email protected] or creating an issue on GitHub.