This branch is developed for face recognition with occlusion, the related paper is as follows.

OCCLUSION ROBUST FACE RECOGNITION BASED ON MASK LEARNING[C]
Weitao, Wan and Jiansheng, Chen
2017 IEEE International Conference on Image Processing (ICIP)

The paper can be downloaded from here.

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Our network architecture is

The generated masks on faces with occlusion are

Face verification on lfw validation set with synthesized square blocks with varying sizes.

• Caffe with center loss imported from https://github.com/ydwen/caffe-face
• Mask Layer
  • src/caffe/layers/mask_layer.cu
  • src/caffe/layers/mask_layer.cpp (CPU mode not supported for now)
  • include/caffe/layers/mask_layer.hpp
• training network
  • face_example/train_centerMask2Pool2_ori.prototxt
  • face_example/face_solver.prototxt

1. Specify your mask size in 'num_output' (the value should equal to height x width of the mask)

    layer {
      name: "mask_ip"
      type: "InnerProduct"
      bottom: "mask_conv3"
      top: "mask_ip"
      param {
        name: "mask_ip_w"
        lr_mult: 1
      }
      param {
        name: "mask_ip_b"
        lr_mult: 2
      }
      inner_product_param {
        num_output: 572     # change it based on your network
        weight_filler {
          type: "constant"
        }
        bias_filler {
          type: "constant"
        }
      }
    }
   

2. Choose the location to insert the mask layer. I placed it after 'pool2'.

    layer {
      name: "mask"
      type: "Mask"
      bottom: "pool2"
      bottom: "mask_2d"
      top: "masked_pool2"
      mask_param {
        scale: 1
      }
    }
   

Weitao Wan([email protected])

Please consider citing the following paper if it helps your research.

@inproceedings{wan2017mask,
  title={OCCLUSION ROBUST FACE RECOGNITION BASED ON MASK LEARNING},
  author={Weitao, Wan and Jiansheng, Chen},
  booktitle={IEEE International Conference on Image Processing (ICIP)},
  pages={},
  year={2017},
  organization={IEEE}
}

Copyright (c) Weitao Wan

All rights reserved.

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Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

• DIY Deep Learning for Vision with Caffe
• Tutorial Documentation
• BVLC reference models and the community model zoo
• Installation instructions

and step-by-step examples.

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}