NIR_VIS_Face_Recognition

NIR-VIS face recognition for CASIA NIR-VIS2.0 dataset, implemented by Pytorch.
Scripts only for testing and reporting performance.
With pretrained LightCNN29V2 and proper preprocessing, Rank1 accuracy can achieve 96.7%
With ArcFace, the Rank1 accuracy can achieve 99.7%

Description

1. extract_features.py
   Apply pretrained deep learning model(LightCNN 9/29) to extract face embedding(128D).
   Compute Rank1 accuracy and accuracy of AR@FAR=0.001.
   It implements the view2 protocal defined by CASIA2.0 dataset.
2. light_cnn.py
   The standard LightCNN model.
   Acess here for more about LightCNN: https://github.com/AlfredXiangWu/LightCNN

Prerequisite

1. Download CASIA NIR-VIS2.0 dataset.
2. Do face detection and face alignment on this dataset, then crop face images to 128*128.
   You can follow MTCNN to do face detection, but some NIR faces will not be detected.
   A better way is to use SSH face detector, which is introduced in this paper: https://arxiv.org/abs/1708.03979
3. Download pretrained LightCNN weight.