This branch is developed for deep face recognition, the related paper is as follows.
A Discriminative Feature Learning Approach for Deep Face Recognition[C]
Yandong Wen, Kaipeng Zhang, Zhifeng Li*, Yu Qiao
European Conference on Computer Vision. Springer International Publishing, 2016: 499-515.
- Oct 13, 2016
- A demo for extracting deep feature by the given model is provided.
- Oct 12, 2016
- The links of face model and features on LFW are available.
model: google drive baidu skydrive
feature: google drive baidu skydrive - The training prototxt of toy example on MNIST are released.
- The links of face model and features on LFW are available.
- Otc 9, 2016
- Original Caffe library
- Center Loss
- src/caffe/proto/caffe.proto
- include/caffe/layers/center_loss_layer.hpp
- src/caffe/layers/center_loss_layer.cpp
- src/caffe/layers/center_loss_layer.cu
- face_example
- face_example/data/
- face_example/face_snapshot/
- face_example/face_train_test.prototxt
- face_example/face_solver.prototxt
- face_example/face_deploy.prototxt
- face_example/extractDeepFeature.m
- mnist_example
- mnist_example/data/
- mnist_example/face_snapshot/
- mnist_example/mnist_train_test.prototxt
- mnist_example/mnist_solver.prototxt
- mnist_example/mnist_deploy.prototxt
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The Installation completely the same as Caffe. Please follow the installation instructions. Make sure you have correctly installed before using our code.
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Download the face dataset for training, e.g. CAISA-WebFace, VGG-Face, MS-Celeb-1M, MegaFace.
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Preprocess the training face images, including detection, alignment, etc. Here we strongly recommend MTCNN, which is an effective and efficient open-source tool for face detection and alignment.
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Creat list for training set and validation set. Place them in face_example/data/
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Specify your data source for train & val
layer { name: "data" type: "ImageData" top: "data" top: "label" image_data_param { source: "face_example/data/###your_list###" } }
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Specify the number of subject in FC6 layer
layer { name: "fc6" type: "InnerProduct" bottom: "fc5" top: "fc6" inner_product_param { num_output: ##number## } }
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Specify the loss weight and the number of subject in center loss layer
layer { name: "center_loss" type: "CenterLoss" bottom: "fc5" bottom: "label" top: "center_loss" loss_weight: ##weight## center_loss_param { num_output: ##number## } }
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Train model
cd $CAFFE-FACE_ROOT ./build/tools/caffe train -solver face_example/face_solver.prototxt -gpu X,Y
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Compile matcaffe by make matcaffe
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Specify the correspinding paths in face_example/extractDeepFeature.m
addpath('path_to_matCaffe/matlab'); model = 'path_to_deploy/face_deploy.prototxt'; weights = 'path_to_model/face_model.caffemodel'; image = imread('path_to_image/Jennifer_Aniston_0016.jpg');
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Run extractDeepFeature.m in Matlab
You are encouraged to cite the following paper if it helps your research.
@inproceedings{wen2016discriminative,
title={A Discriminative Feature Learning Approach for Deep Face Recognition},
author={Wen, Yandong and Zhang, Kaipeng and Li, Zhifeng and Qiao, Yu},
booktitle={European Conference on Computer Vision},
pages={499--515},
year={2016},
organization={Springer}
}
Copyright (c) Yandong Wen
All rights reserved.
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