oooh-name / awesome-face-detection

Compare with various detectors - dlib, ocv, ocv-dnn, mtcnn-pytorch, face_recognition

Awesome face detection

Compare face detectors - Dlib, OpenCV, Others..

We are neighborhood

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Processing time

Test image size : HD (720p)

We wanted to check processing time on same condition. but It couldn't becasue each method demand different input size. (ex. opencv dnn use 300x300 bgr image.)

So, Each code has a different image size.

ocv-dnn : 300x300
ocv-haar, dlib-hog, dlib-cnn, fr-hog, fr-cnn : VGA(640x360)
mtcnn : HD(1280x720)

Test on Intel i7-6700K & GTX1080.

ocv-dnn	ocv-haar	dlib-hog	dlib-cnn	fr-hog	fr-cnn	mtcnn
17.79ms	42.31ms	108.61ms	42.17ms	108.50ms	39.91ms	334.38ms

Test on MacBook pro retina 2014 mid.

ocv-dnn	ocv-haar	dlib-hog	dlib-cnn	fr-hog	fr-cnn	mtcnn
46.53ms	88.47ms	174.81ms	3276.62ms	174.63ms	3645.53ms	928.752ms

Requirements

• Python 3.6
• OpenCV 3.4.0 (option: build from src with highgui)
• Dlib 19.10.0
• face_recognition 1.2.1
• pytorch 0.3.1

Usage

First, install libs

pip install opencv-contrib-python
pip install torch
pip install dlib
pip install face_recognition

Second, check run-time for each algorithm.

./run.sh

Of course, You can execute each file. and watch the result image (need opencv high gui)

python dlib-hog.py

Now, Select face detector you need!

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Reference

opencv haar cascade

• https://docs.opencv.org/3.4.1/d7/d8b/tutorial_py_face_detection.html

opencv caffe based dnn (res-ssd)

• https://github.com/opencv/opencv/tree/master/samples/dnn

dlib hog

• http://dlib.net/

dlib cnn

• http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

face-recognition (dlib-based)

• https://github.com/ageitgey/face_recognition

mtcnn

• https://github.com/TropComplique/mtcnn-pytorch (code)
• https://arxiv.org/abs/1604.02878 (paper)