I was just looking for different face detection methods and found a useful library for it called : dlib python bindings
Most of tutorials will provide only one method of face detection which is HOG+SVM based.
While the HOG+SVM based face detector has been around for a while and has gathered a good amount of users I accidentally came across it while browsing through dlib’s github repository.
It's just a bunch of python scripts. Together they:
- Detect multiple faces using HOG + SVM method in real time as well as from image
- Detect multiple faces using CNN method in real time as well as from image
- Result comparison
- Face detection with deep learning models supported in CV2
The comparison of two different face detectors are as follows :
-
Dlib's get_frontal_face : based on HOG + SVM classifier
- It's so much fast so can be used in real time
- Unable to detecting faces at odd angles
- Most of the computer vision engineers use
-
Dlib's cnn_face_detection_model_v1 : CNN architecture trained model mmod_human_face_detector.dat
- It's very poor in speed and computation
- Far better than dlib's inbuilt model as it's more accurate
- Less popular
Dataset used to train model : http://dlib.net/files/data/
To start with project just follow the few steps
$ git clone https://github.com/keyurr2/face-detection.git
$ pip install -r requirements.txt
$ cd into <project-folder>
$ python <script-name> -i <image> -w <weightage>
NOTE: How to run each script is mentioned in script itself.
- Keyur Rathod (keyur.rathod1993@gmail.com)
This project is licensed under the MIT License - see the LICENSE.md file for details