Awesome face detection

Compare face detectors - Dlib, OpenCV, Others..

We are neighborhood

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)
s3fd : HD --> 1/8 scale. low resolution but awesome performance!
insightface(retianface_r50_v1) : VGA(640x360)

Test on Intel i7-6700K & GTX1080.

ocv-dnn	ocv-haar	dlib-hog	dlib-cnn	fr-hog	fr-cnn	mtcnn	S3FD	insightface
17.79ms	42.31ms	108.61ms	42.17ms	108.50ms	39.91ms	334.38ms	31.87ms	21.49ms

Test on Intel Xeon E5-1660 & NVIDIA GV100.

ocv-dnn	ocv-haar	dlib-hog	dlib-cnn	fr-hog	fr-cnn	mtcnn	S3FD	insightface
16.76ms	32.95ms	124.35ms	24.58ms	121.73ms	24.88ms	292.45ms	31.07ms	TBA

Test on MacBook pro 2018 i5.

ocv-dnn	ocv-haar	dlib-hog	dlib-cnn	fr-hog	fr-cnn	mtcnn	S3FD	insightface
46.53ms	88.47ms	174.81ms	3276.62ms	174.63ms	3645.53ms	928.75ms	271.18ms	TBA

Requirements

Python 3.7
OpenCV 4.1.1 (option: build from src with highgui)
Dlib 19.17.0
face_recognition 1.2.3
pytorch 1.2.0
mxnet-cu100 1.5.1.post0

Usage

First, install libs

pip install opencv-contrib-python
pip install torch
pip install dlib
pip install face_recognition
pip install easydict
pip install mxnet-cu100
pip install insightface

Second, prepare weight file (s3fd)

download s3fd weight: https://drive.google.com/open?id=1Dyr-s3mAQEj-AXCz8YIIYt6Zl3JpjFQ7