This is demo of face recoginition system backend. We heavily depend on some open source code, test on some implementations on benchmark dataset and finally we integrate them into our backend system.
Frontend is developed by Gao Zhongye.
- Anaconda 5.0 Python 3.6.0
- Pytorch 0.2.0
- tensorflow 1.2.0
- keras 2.1.2
- openface
- dlib 19.7.0
- django 1.11.7
- djangorestframework 3.7.3
The project is mainly based on Django project structure.
- face_algorithm: core face recognition algoritm
- face_id_backend: django configuration
- face_recognition: django app
- media: face img and feature vector file
- test_json: json files, used in testing communication
- manage.py
- README.md
- center loss: untrained
- facenet tensorflow version: on testing
- joint bayes: train on openface and VGGface feature vector
- light cnn: on testing
- MTCNN keras version: conflict with TensorFlow
- MTCNN pytorch version: implement with sphereface
- saim 2-channel CNN: untrained
- sphereface pytorch version: test on LFW successfully
- VGG face keras version: implement on django system, current best
- openface: implement on django system
- dlib align: implement on django system
cd face_id_backend
python manage.py runserver 0.0.0.0：8888
cd face_id_backend/face_algoritm
python lfw.py
you need to modify code and specify model you want to test, we will add argparser in the future.
- A Discriminative Feature Learning Approach for Deep Face Recognition
- FaceNet: A Unified Embedding for Face Recognition and Clustering
- Bayesian Face Revisited: A Joint Formulation
- A Light CNN for Deep Face Representation with Noisy Labels
- Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- SphereFace: Deep Hypersphere Embedding for Face Recognition
- Learning to Compare Image Patches via Convolutional Neural Networks
- Deep Face Recognition
- Deep Learning Face Representation from Predicting 10,000 Classes
- DeepID3: Face Recognition with Very Deep Neural Networks
- One Millisecond Face Alignment with an Ensemble of Regression Trees
- Large-Margin Softmax Loss for Convolutional Neural Networks
- Siamese Neural Networks for One-shot Image Recognition
- Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?