yu9s/pewface

Official Implementation of the paper "μPEWFace: Parallel Ensembling Weighted Deep Convolutional Neural Networks with Novel Adaptive Loss Functions for Face-based Authentication"

μPEWFace

The official implementation for the "μPEWFace: Parallel Ensembling Weighted Deep Convolutional Neural Networks with Novel Adaptive Loss Functions for Face-based Authentication"

We firstly investigate and analyzes the effect of several effective loss functions based on softmax on DCNN with the ResNet50 architecture. We then propose a parallel ensemble learning, namely μPEWFace, by taking advantage of recent novel face recognition methods: AdaFace, MagFace, ElasticFace. μPEWFace elaborates on the weighted-based voting mechanism that utilizes non-optimal pre-trained models to show the proposed method’s massive potential in improving face-based authentication performa. In addition, we propose to perform the matching phase for each μPEWFace model in parallel on both GPU and CPU. The results of our experiments achieve state-of-the-art figures, which show the proposed method’s massive potential in improving face recognition performance.

What's New

Getting Started

Installation

git clone https://github.com/ewigspace1910/PEWFace.git
cd PEWFace
pip install -r requirements.txt

Prepare Datasets

• We use CASIA-Webface for training and some available test sets including LFW, CFP-FP, AgeDB, CALFW, CPLFW for benchmark. All datasets is contributed from Insightface

• Download and extract into data folder. Please unzip data and prepare like

PEWFace/data
├── casia-webface
│   └── 00000
│   └── 00001
│   └...
├── lfw
│   └── 00001.jpg
│   └...
├── cfp_fp
│   └── 00001.jpg
│   └...
├── ...
│
├── images_lists.txt
├── lfw_pair.txt
├── cfp_fp_pair.txt
└── ...

Training

• We re-implement MagFace, ElasticFace, AdaFace on 1 Tesla T4 GPU. We use 112x112 sized images and adopt only resnet50 architecture(with BN-Dropout-FC-BN header) for training. Because of 16G GPU Ram, we set batch size to 128 instead of 512 like others.

  bash script/train.sh

Evaluation

In this stage, we will conduct an ensemble from trained models by Weight-based Voting mechanism. Then, we apply parallel processing to the inference processing of the ensemble.

1. Evaluate the effectiveness of Ensemble.

   • Test the individual trained model (optional):

   python examples/test.py --c configs/softmax.yaml --p ./save/softmax/ckpt/checkpoint.pth

   • Test the Ensemble:

   bash script/test_ensemble.sh

2. Evaluate the effectiveness of parallel processing on both GPU and CPU.

   • Test performance of parallel processing:

   bash script/test_parallel.sh

Reported Results

• we will upload when the paper is published

Citation