Light CNN for Deep Face Recognition, in Tensorflow

A Tensorflow implementation of A Light CNN for Deep Face Representation with Noisy Labels from the paper by Xiang Wu

Updates

• Jan 9, 2018
  • Add cleaned training list 10K and 70K.
• Sep 20, 2017
  • Add model and evaluted code.
  • Add training code.
• Sep 19, 2017
  • The repository was built.

Datasets

• Training data
  • Download face dataset MS-Celeb-1M (Aligned).
  • All face images are RGB images and resize to 122x144
  • Download MS-Celeb-1M cleaned image_list 10K, 70K
• Testing data
  • Download aligned LFW (122*144) images and list

Training

• Add

Evaluation

• Download LCNN-29 model, this model's performance on LFW:98.2% (100%-EER)
• Download LFW features

Performance

The Light CNN performance on lfw 6,000 pairs.

Model	traing data	method	Acc	100% - EER	TPR@FAR=1%	TPR@FAR=0.1%	TPR@FAR=0
LightCNN-29 (Wu Xiang)	70K/-	Softmax	-	99.40%	99.43%	98.67%	95.70%
LightCNN-29 (Tensorflow)	10K/-	Softmax	98.36%	98.2%	97.73%	92.26%	60.53%
LightCNN-29 (Tensorflow)	10K/-	Softmax+L2+PCA	98.76%	98.66%	98.36%	97%	79.33%
LightCNN-29 (Tensorflow)	10K/-	Softmax+L2+PCA+[b]	98.95%	98.8%	98.76%	97.16%	83.36%
LightCNN-29 (Tensorflow)	10K/-	Softmax_enforce+L2+PCA+[b]	99.01%	98.96%	98.96%	95.83%	90.23%

Model	traing data	method	Acc	100% - EER	TPR@FAR=1%	TPR@FAR=0.1%	TPR@FAR=0
LightCNN-29 (Wu Xiang)	70K/-	Softmax	-	99.40%	99.43%	98.67%	95.70%
LightCNN-29 (Tensorflow)	70K/-	Softmax_enforce+L2+PCA	99.18%	98.9%	98.86%	97.9%	94.03%
LightCNN-29 (Tensorflow)	70K/-	Softmax_enforce+L2+PCA+[a]	99.48%	99.43%	99.56%	98.26%	94.53%

Some improved solutions:

• [a] It can be further improved by manaully aligned these images which are mis-algined in LFW
• [b] It can be further improved by doing mutiple-crop, e.g. 25 crops for per image
• [c] It can be further improved by ensemble different models
• [d] It can be further improved by adding metric learning method for similarity caculation

Referencs

• Original Light CNN implementation (caffe).