mindspore/model_zoo/research/cv/MaskedFaceRecognition

• Contents
• • Masked Face Recognition Description
  • Dataset
  • Training Dataset
  • Evaluating Dataset
  • Environment Requirements
  • Script Description
  • Training
  • Evaluation
  • ModelZoo Homepage

Contents

• Masked Face Recognition Description
• Dataset
• Environment Requirements
• Script Description
• Training
• Evaluation
• ModelZoo Homepage

Masked Face Recognition Description

This is a MindSpore implementation of Masked Face Recognition with Latent Part Detection (ACM MM20) by Feifei Ding, Peixi Peng, Yangru Huang, Mengyue Geng and Yonghong Tian.

Masked Face Recognition aims to match masked faces with common faces and is important especially during the global outbreak of COVID-19. It is challenging to identify masked faces since most facial cues are occluded by mask.

Latent Part Detection (LPD) is a differentiable module that can locate the latent facial part which is robust to mask wearing, and the latent part is further used to extract discriminative features. The proposed LPD model is trained in an end-to-end manner and only utilizes the original and synthetic training data.

Dataset

Training Dataset

We use CASIA-WebFace Dataset as the training dataset. After downloading CASIA-WebFace, we first detect faces and facial landmarks using MTCNN and align faces to a canonical pose using similarity transformation. (see: MTCNN - face detection & alignment).

Collecting and labeling realistic masked facial data requires a great deal of human labor. To address this issue, we generate masked face images based on CASIA-WebFace. We generate 8 kinds of synthetic masked face images to augment training data based on 8 different styles of masks, such as surgical masks, N95 respirators and activated carbon masks. We mix the original face images with the synthetic masked images as the training data.

Evaluating Dataset

We use PKU-Masked-Face Dataset as the evaluating dataset. The dataset contains 10,301 face images of 1,018 identities. Each identity has masked and common face images with various orientations, lighting conditions and mask types. Most identities have 5 holistic face images and 5 masked face images with 5 different views: front, left, right, up and down.

The directory structure is as follows:

.
└─ dataset
  ├─ train dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...
  ├─ test dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend processor. If you want to get Ascend , please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore Python API

Script Description

The entire code structure is as following:

└─ face_recognition
  ├── README.md                             // descriptions about face_recognition
  ├── scripts
  │   ├── run_train.sh                      // shell script for training on Ascend
  │   ├── run_eval.sh                       // shell script for evaluation on Ascend
  ├── src
  │   ├── dataset
  │   │   ├── Dataset.py                    // loading evaluating dataset
  │   │   ├── MGDataset.py                  // loading training dataset
  │   ├── model
  │   │   ├── model.py                      // lpd model
  │   │   ├── stn.py                        // spatial transformer network module
  │   ├── utils
  │   │   ├── distance.py                   // calculate distance of two features
  │   │   ├── metric.py                     // calculate mAP and CMC scores
  ├─ config.py                              // hyperparameter setting
  ├─ train_dataset.py                       // training data format setting
  ├─ test_dataset.py                        // evaluating data format setting
  ├─ train.py                               // training scripts
  ├─ test.py                                // evaluation scripts

Training

sh scripts/run_train.sh [USE_DEVICE_ID]

You will get the loss value of each epoch as following in "./scripts/data_parallel_log_[DEVICE_ID]/outputs/logs/[TIME].log" or "./scripts/log_parallel_graph/face_recognition_[DEVICE_ID].log":

epoch[0], iter[100], loss:(Tensor(shape=[], dtype=Float32, value= 50.2733), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 32768)), cur_lr:0.000660, mean_fps:743.09 imgs/sec
epoch[0], iter[200], loss:(Tensor(shape=[], dtype=Float32, value= 49.3693), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 32768)), cur_lr:0.001314, mean_fps:4426.42 imgs/sec
epoch[0], iter[300], loss:(Tensor(shape=[], dtype=Float32, value= 48.7081), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 16384)), cur_lr:0.001968, mean_fps:4428.09 imgs/sec
epoch[0], iter[400], loss:(Tensor(shape=[], dtype=Float32, value= 45.7791), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 16384)), cur_lr:0.002622, mean_fps:4428.17 imgs/sec

...
epoch[8], iter[27300], loss:(Tensor(shape=[], dtype=Float32, value= 2.13556), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 65536)), cur_lr:0.004000, mean_fps:4429.38 imgs/sec
epoch[8], iter[27400], loss:(Tensor(shape=[], dtype=Float32, value= 2.36922), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 65536)), cur_lr:0.004000, mean_fps:4429.88 imgs/sec
epoch[8], iter[27500], loss:(Tensor(shape=[], dtype=Float32, value= 2.08594), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 65536)), cur_lr:0.004000, mean_fps:4430.59 imgs/sec
epoch[8], iter[27600], loss:(Tensor(shape=[], dtype=Float32, value= 2.38706), Tensor(shape=[], dtype=Bool, value= False), Tensor(shape=[], dtype=Float32, value= 65536)), cur_lr:0.004000, mean_fps:4430.37 imgs/sec

Evaluation

sh scripts/run_eval.sh [USE_DEVICE_ID]

You will get the result as following in "./scripts/log_inference/outputs/models/logs/[TIME].log":
[test_dataset]: zj2jk=0.9495, jk2zj=0.9480, avg=0.9487

model	mAP	rank1	rank5	rank10
Baseline	27.09	70.17	87.95	91.80
MG	36.55	94.12	98.01	98.66
LPD	42.14	96.22	98.11	98.75

ModelZoo Homepage

Please check the official homepage.