🚧This repo is no longer maintained, it can be seamlessly replaced with the new repo batch-face with the exact same usage.🚧

The batch-face provides more utility for face detection, face alignment and face reconstruction.

Face Detection

Fast and reliable face detection with RetinaFace.

This repo provides the out-of-box RetinaFace detector.

Requirements

• Python 3.5+ (it may work with other versions too)
• Linux, Windows or macOS
• PyTorch (>=1.0)

While not required, for optimal performance it is highly recommended to run the code using a CUDA enabled GPU.

Install

The easiest way to install it is using pip:

pip install git+https://github.com/elliottzheng/face-detection.git@master

Usage

Detect face and five landmarks on single image

from skimage import io
from face_detection import RetinaFace

detector = RetinaFace()
img= io.imread('examples/obama.jpg')
faces = detector(img)
box, landmarks, score = faces[0]

Running on CPU/GPU

In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.

from face_detection import RetinaFace
# 0 means using GPU with id 0 for inference
# default -1: means using cpu for inference
detector = RetinaFace(gpu_id=0) 

Batch input for faster detection

All the input images must of the same size.

Detector with CUDA process batch input faster than the same amount of single input.

from skimage import io
from face_detection import RetinaFace

detector = RetinaFace()
img= io.imread('examples/obama.jpg')
all_faces = detector([img,img]) # return faces list of all images
box, landmarks, score = all_faces[0][0]

Reference

• Network and pretrained model are from biubug6/Pytorch_Retinaface

@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}