https://github.com/yxu0611/Tensorflow-implementation-of-LCNN
A Tensorflow implementation of "A Light CNN for Deep Face Representation with Noisy Labels"
https://github.com/yxu0611/Tensorflow-implementation-of-LCNN
face-recognition face-verification lightcnn tensorflow
Last synced: about 1 month ago
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A Tensorflow implementation of "A Light CNN for Deep Face Representation with Noisy Labels"
- Host: GitHub
- URL: https://github.com/yxu0611/Tensorflow-implementation-of-LCNN
- Owner: yxu0611
- Created: 2017-09-19T10:46:52.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-01-09T05:10:49.000Z (over 7 years ago)
- Last Synced: 2024-11-02T10:34:09.591Z (6 months ago)
- Topics: face-recognition, face-verification, lightcnn, tensorflow
- Language: Python
- Homepage:
- Size: 111 KB
- Stars: 78
- Watchers: 10
- Forks: 12
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
# Light CNN for Deep Face Recognition, in Tensorflow
A Tensorflow implementation of [A Light CNN for Deep Face Representation with Noisy Labels](https://arxiv.org/abs/1511.02683) 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)](http://www.msceleb.org/download/aligned).
- All face images are RGB images and resize to **122x144**
- Download MS-Celeb-1M cleaned image_list [10K](https://1drv.ms/t/s!AleP5K29t5x7ge87YS8Ue92h8JDDMw), [70K](https://1drv.ms/t/s!AleP5K29t5x7gfEu_3My1D3lgDhLlQ)
- Testing data
- Download aligned LFW (122*144) [images](https://1drv.ms/u/s!AleP5K29t5x7ge88rngfpitnvpkZbw) and [list](https://1drv.ms/t/s!AleP5K29t5x7ge9DV6jfHo392ONwCA)
## Training
- Add
## Evaluation
- Download [LCNN-29 model](https://1drv.ms/f/s!AleP5K29t5x7ge89GqB3Ue_Pe5rN3A), this model's performance on LFW:98.2% (100%-EER)
- Download [LFW features](https://1drv.ms/u/s!AleP5K29t5x7ge9ElofW_tDzxCq5sw)
## 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)](https://github.com/AlfredXiangWu/face_verification_experiment).