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.
Based on the caffe toolbox, we organize the source files as follows:
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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.
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- Contact: Please send comments to Kaili Zhao (kailizhao@bupt.edu.cn)
- Citation: If you use this code in your paper, please cite the following:
@inproceedings{zhao2016deep,
title={Deep Region and Multi-Label Learning for Facial Action Unit Detection},
author={Zhao, Kaili and Chu, Wen-Sheng and Zhang, Honggang},
booktitle={CVPR},
year={2016}
}