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Free and open source face detection and recognition with deep learning. Based on the MTCNN and ResNet Center-Loss
DFace is an open source software for face detection and recognition. All features implemented by the pytorch (the facebook deeplearning framework). With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows developer to change the way your network behaves arbitrarily with zero lag or overhead. DFace inherit these advanced characteristic, that make it dynamic and ease code review.
DFace support GPU acceleration with NVIDIA cuda. We highly recommend you use the linux GPU version.It's very fast and extremely realtime.
Our inspiration comes from several research papers on this topic, as well as current and past work such as Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks and face recognition topic FaceNet: A Unified Embedding for Face Recognition and Clustering
MTCNN Structure
If you want to contribute to DFace, please review the CONTRIBUTING.md in the project.We use Slack for tracking requests and bugs. Also you can following the QQ group 681403076 or my wechat jinkuaikuai005
- Based on cener loss or triplet loss implement the face conpare. Recommended Model is ResNet inception v2. Refer this Paper and FaceNet
- Face Anti-Spoofing, distinguish from face light and texture。Recomend with the LBP algorithm and SVM.
- 3D mask Anti-Spoofing.
- Mobile first with caffe2 and c++.
- Tensor rt migration.
- Docker support, gpu version
DFace has two major module, detection and recognition.In these two, We provide all tutorials about how to train a model and running. First setting a pytorch and cv2. We suggest Anaconda to make a virtual and independent python envirment.
- cuda 8.0
- anaconda
- pytorch
- torchvision
- cv2
- matplotlib
Also we provide a anaconda environment dependency list called environment.yml in the root path. You can create your DFace environment very easily.
conda env create -f path/to/environment.ymlIf you are interested in how to train a mtcnn model, you can follow next step.
MTCNN have three networks called PNet, RNet and ONet.So we should train it on three stage, and each stage depend on previous network which will generate train data to feed current train net, also propel the minimum loss between two networks. Please download the train face datasets before your training. We use WIDER FACE and CelebA
- Generate PNet Train data and annotation file
python src/prepare_data/gen_Pnet_train_data.py --dataset_path {your dataset path} --anno_file {your dataset original annotation path}- Assemble annotation file and shuffle it
python src/prepare_data/assemble_pnet_imglist.py- Train PNet model
python src/train_net/train_p_net.py- Generate RNet Train data and annotation file
python src/prepare_data/gen_Rnet_train_data.py --dataset_path {your dataset path} --anno_file {your dataset original annotation path} --pmodel_file {yout PNet model file trained before}- Assemble annotation file and shuffle it
python src/prepare_data/assemble_rnet_imglist.py- Train RNet model
python src/train_net/train_r_net.py- Generate ONet Train data and annotation file
python src/prepare_data/gen_Onet_train_data.py --dataset_path {your dataset path} --anno_file {your dataset original annotation path} --pmodel_file {yout PNet model file trained before} --rmodel_file {yout RNet model file trained before}- Generate ONet Train landmarks data
python src/prepare_data/gen_landmark_48.py- Assemble annotation file and shuffle it
python src/prepare_data/assemble_onet_imglist.py- Train ONet model
python src/train_net/train_o_net.pypython test_image.pyTODO
