Face recognition with Coral EdgeTPU Support based on MobileFacenet

Mobilefacenet with Tensorflow-2, EdgeTPU models also supplied for running model on Coral EdgeTPU

Introduction

Tensorflow 2 version of mobilefacenet from MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

Demo

Video Link

Running 60 fps on Desktop with Coral TPU, and around 24 fps on raspberry pi

Usage

Dataset

Use the same dataset as used in Mobilefacenet-Pytorch to train. CASIA is used for training and LFW is used for testing.

Training

Change the directory pointing to image dataset in train.py. I trained the model directly with ArcFace by setting RESUME to False but it is worthwhile to try out pretraining with softmax loss

I added an example to add extra header to perform classification using generated embedding, here I use generated embedding to make prediction on whether a person is wearing mask. You can have more fun by using another dataset

Result

Trained model is evaluate on each epoch use LFW dataset and I got 99.3% accuracy without pretraining

Credit to

1. Playground by Qihang Zheng: https://github.com/zhen8838/playground
2. Mobilefacenet-Pytorch: https://github.com/Xiaoccer/MobileFaceNet_Pytorch
3. MobileFaceNet-Keras: https://github.com/TMaysGGS/MobileFaceNet-Keras