https://github.com/william-versatile/face-recognition-python
Face recognition by using python
https://github.com/william-versatile/face-recognition-python
python recognition
Last synced: about 1 year ago
JSON representation
Face recognition by using python
- Host: GitHub
- URL: https://github.com/william-versatile/face-recognition-python
- Owner: William-versatile
- Created: 2024-03-08T02:25:17.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-03-08T02:25:43.000Z (over 2 years ago)
- Last Synced: 2025-02-10T15:17:06.799Z (over 1 year ago)
- Topics: python, recognition
- Language: Python
- Homepage:
- Size: 104 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Face Recognition based on DeepID
Implementation of DeepID based on the paper "Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 1891-1898."
## Dataset preparation
LFW - refer to sklearn.dataset
Facescrub - http://vintage.winklerbros.net/facescrub.html
Cropped only faces, separate them into train, val, and test set with ratio of 0.7, 0.1, 0.2 respectively
## Current state
Only done face identification, working on face verification
## Training
Initially learning rate of 0.01 using exponential decay on 100000 steps/0.9 decay rate
Monitor the training graph, if it stays at a loss/accuracy for a long time, initialise learning rate with 0.005 or lower (my guess on it, i think it is because it reaches a local minimum gradient, couldn't go deeper)
## Reminder
1. Small dataset will be easily overfit as there is nothing much to "learn" from the dataset
2. Due to Internet speed and storage problem, I choose a smaller than CASIA dataset (stated in the paper), but bigger than LFW which is facescrub
3. My code is in continue training state, if you want a new training, comment the "load" code
## Performance
Training on LFW - maximum of 80% accuracy (only 68 classes, I choose minimum of 10 faces)
Training on Facescrub - still training, but reached 75% accuracy by the time I commit (530 classes)
## Contact
Email: kamwoh@gmail.com
## Reference
[1]. https://github.com/RiweiChen/DeepFace
[2]. https://github.com/stdcoutzyx/DeepID_FaceClassify
[3]. Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 1891-1898.