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Deep Region and Multi-label Learning for Facial Action Unit Detection (CVPR16)
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This repository provides the codes for the CVPR16 paper, “Deep Region and Multi-Label Learning for Facial Action Unit Detection". This code aims for training a convolutional network that contains a region layer for specializing the learned kernels on different facial regions, and meanwhile utilizes a multi-label cross-entropy to jointly learn 12 AUs. This implementation is based on Caffe Toolbox.

File structure

Based on the caffe toolbox, we organize the source files as follows:

  • include/caffe/: Header files that contains the declaration of our implemented layers

  • prototxt/: Network architecture we used to compuare and report in our paper

  • src/caffe/layers/: Source files of our implemented layers

    • box_layer.*: Slice a 160x160 response map into an 8x8 uniform grid.

    • image_data_layer_multilabel.cpp: Load multiple labels for one image.

    • multi_sigmoid_cross_entropy_loss_layer.*: Multi-label loss.

    • splice.*: Concatenate 20 8x8 uniform grids to a 160x160 feature map.

More info

  • Contact: Please send comments to Kaili Zhao (
  • Citation: If you use this code in your paper, please cite the following:
  title={Deep Region and Multi-Label Learning for Facial Action Unit Detection},
  author={Zhao, Kaili and Chu, Wen-Sheng and Zhang, Honggang},
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