Face_Recognition

Introduction

My backbone network is Resnet-50 V1 ,whose output dimension is 512, trained by "ArcLoss" loss layer. My training data is provided by Trillion Pairs which is cleaned from MS-Celeb-1M and Asian-Celeb.The best performance for face recognition so far is 1.LFW: 99.3% accurace 2. Megaface: 93% true accept rate under 0.0001% false accept rate on 1:1 verification

Contents

Model

Backbone network : Resnet-50 V1 with 512 dim output. My [Pre-trained model] (https://github.com/tensorflow/models/tree/master/research/slim) is provided by Tensorflow

Loss layer : Arc Loss layer

Training Data

Training data is downloaded from Trillion Pairs (both MS-Celeb-1M-v1c and Asian-Celeb)

All face images are aligned by Dlib: 5-point landmark and and cropped to 224x224 and packed in binary format

Train

Environment : windows 10 + Python 3.5(installed by Anaconda) + Tensorflow 1.5.0 GPU version

GPU : 1080Ti

Batch Size : 50

How to run training

Step 1: Put the packed binary format data in the floder named "training"

Step 2: run train_Arc_loss_multi_task.py

python train_Arc_loss_multi_task.py

In the training process, trained model and training log will saved at "Model" floder

It will create a floder named loss_debug to save the loss of each step.

About learning rate decay.Instead of steps or polynomial decay, the learning rate will dacay automatically when loss stop improving. it will make the network trained better because you never know how many steps the training need before you train it. How to determine loss stop improving

On going

Training with larger batch size

Bigger batch size seems to make the network trained better

Multi-task training

I plan to train three tasks which are FR, gender and age classification in the same network. They will share the resnet-50 network. I have finished the code.