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Face Recognition

Introduction

This repository contains training and inference code for face recognition neural networks. The face recognition network is based on the MobileFaceNet architecture equipped with Squeeze-and-Excitation blocks. Landmark regression network is a simple CNN consisting from several stacked convolution and pooling layers. The code supports conversion to the ONNX* format.

Model Name LFW accuracy GFlops MParams Links
face-reidentification-retail-0095 0.9947 0.588 1.107 shapshot, configuration file
MibileFaceNetSE_2x 0.9942 1.155 2.197 shapshot, configuration file

Setup

Prerequisites

  • Ubuntu* 16.04
  • Python* 3.5.2
  • PyTorch* 1.3 or higher
  • OpenVINO™ 2020 R3 (or newer) with Python API

Installation

To create and activate virtual Python environment follow installation instructions

Data preparation

  1. For a face-recognition training, download the VGGFace2 data. We will refer to this folder as $VGGFace2_ROOT. Then align and crop this dataset using the provided annotation and script:
mv $VGGFace2_ROOT/test/* $VGGFace2_ROOT/train/

cat $VGGFace2_ROOT/meta/train_list.txt $VGGFace2_ROOT/meta/test_list.txt >> $VGGFace2_ROOT/meta/all_list.txt

cat $VGGFace2_ROOT/bb_landmark/loose_landmark_train.csv $VGGFace2_ROOT/bb_landmark/loose_landmark_test.csv >> $VGGFace2_ROOT/bb_landmark/loose_landmark_all.csv

cat $VGGFace2_ROOT/bb_landmark/loose_bb_train.csv $VGGFace2_ROOT/bb_landmark/loose_bb_test.csv >> $VGGFace2_ROOT/bb_landmark/loose_bb_all.csv

python3 ../../../external/deep-object-reid/convert_vgg_face_2.py
                      --data-root  $VGGFace2_ROOT
                      --output-dir $root/vggface2
  1. For a face-recognition evaluation, download the LFW data and LFW landmarks. Place everything in one folder, which will refer to as $LFW_ROOT.

The overall directory structure should look as follows:

root
├── vggface2
│   ├── all_list.txt
│   ├── n0000002
│   └── ....
│
└── lfw
    ├── lfw_landmark.txt
    ├── pairs_bench_crossval.txt
    ├── ...

Configuration Files

The script for training and inference uses a configuration file default_config.py, which consists of default parameters. This file also has description of parameters. Parameters that you wish to change must be in your own configuration file. Example: face-reidentification-retail-0095.yaml

Training

To start training, create or choose a configuration file and use the main.py script.

Example:

python ../../../external/deep-object-reid/tools/main.py \
    --root /path/to/datasets/directory/root \
    --config configs/face-reidentification-retail-0095.yaml

Test

To test your network on the LFW dataset, set in a configuration file the test.evaluate parameter to True and run a command like the one used for training.

Training and evaluation of landmarks regression model

To train the landmarks regression model we need to use the original unaligned VGGFace2 dataset:

python ../../../external/deep-object-reid/projects/landmarks_regression/train.py \
                --train_data_root $VGGFace2_ROOT/train/ \
                --train_list $VGGFace2_ROOT/meta/train_list.txt \
                --train_landmarks $VGGFace2_ROOT/bb_landmark/ \
                --dataset vgg --snap_folder <snapshots_folder>

To evaluate the trained model run the corresponding script:

python3 evaluate.py \
          --dataset vgg \
          --val_data_root $VGGFace2_ROOT/train/ \
          --val_list /mnt/big_ssd/VGGFace2/meta/test_list.txt \
          --val_landmarks /mnt/big_ssd/VGGFace2/bb_landmark/ \
          --snapshot <path to snashot>

Note: VGGFace2 contains auto-generated annotation of facial landmarks, therefore, this annotation is not very precise, but it's enuogh to train a decent model.

Convert a PyTorch Model to the OpenVINO™ Format

To convert the obtained face recognition model, the following:

python ../../../external/deep-object-reid/tools/convert_to_onnx.py \
    --config /path/to/config/file.yaml \
    --output-name /path/to/output/model \
    --verbose

Name of the output model ends with .onnx automatically. By default, the output model path is model.onnx. Be careful about the load_weights parameter in the configurations file. The verbose argument is non-required and switches on detailed output in conversion function.

To convert the trained landmark regression model launch the script:

python3 convert_onnx.py --snap <path to snapshot> --output_dir <output directory>

To convert the obtained ONNX format to OpenVINO™ IR, the following steps should be done:

  • Make sure that OpenVINO environment is initialized.
  • Launch the script from Model Optimizer directory:
python3 mo_onnx.py --input_model <path to obtained onnx> --output_dir <path to output dir> --reverse_input_channels --input_shape [1,3,128,128] --scale 255
``