The official implementation code of paper: "AU R-CNN：Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection"
published in Neurocomputing: https://www.sciencedirect.com/science/article/pii/S0925231219305338
google drive download link: PDF.
Arxiv version: https://arxiv.org/abs/1812.05788
The software dependencies are listed in the following, where the dlib library is the most important, you can install it from compiling the source code and then use python setup.py install to install it. Or you can just type pip install dlib.
CUDA
cudnn
cupy
chainer 4.0 or above
dlib
bidict
memcached （independent software which is intended to store AU bounding box coordinates, it must be installed for boosting training speed by using cache).
pylibmc (python client to store or get AU bounding box coordinates from memcached)
Opencv (conda install -c menpo opencv）
overrides
cython
lru_dict
six
Anaconda 3.6 version python install example:
-
Use
conda install xxxorpip install xxxto install python library. -
How to install
memcached. Installing memcached like a recursive call, usepylibmcdependent onlibmemcached,libmemcacheddependent onmemcached,memcacheddependent onlibevent.
sudo apt-get install gcc make binutils
sudo apt-get install python python-all-dev
sudo apt-get install memcached
wget https://launchpad.net/libmemcached/1.0/1.0.18/+download/libmemcached-1.0.18.tar.gz
tar zxf libmemcached-1.0.18.tar.gz
cd libmemcached-1.0.18
./configure --prefix=/usr/local/libmemcached
make && make install
LIBMEMCACHED=/usr/local/libmemcached pip install pylibmc
NOTE: if you are using CentOS or Debian. Maybe the version of libevent is low，so you need to install it from source code. After installing it, you should use the command of ln -s /usr/lib/libevent-1.3.so.1 /usr/local/lib/libevent-1.3.so.1 to link. Then you can enter python in the terminal, and type import pylibmc in python console. If no error output is displayed, then you successfully installed memcached and pylibmc.
All training necessary files can be downloaded from https://drive.google.com/open?id=1y-yjOPuo7OcXA_bbNIZ0bfmV72mOBXON. I have also uploaded these files (~1.3G) into https://github.com/sharpstill/AU_R-CNN/tree/master/necessary_train_files_for_AU_R-CNN, so git clone to download this repository may be time expensive.
First, you must modify the config.py to modify all the file paths to be consistent with your machine environment. Then, you must place the downloaded training necessary files to the correct folder paths. Finally, the file for training AU R-CNN is located in AU_rcnn/train.py. To best of our knowledge, the F1 score of BP4D (3-fold testing protocol) is 63%, this is state-of-the-art result.
I have uploaded all the pre-trained models into https://cloud.tsinghua.edu.cn/d/8190d64e83ef43b7ab3f/, which can be downloaded. I also uploaded part of the pre-trained models onto https://drive.google.com/drive/folders/1Q8tx98QIQnT1yz1uSrCxszkobs8MSgrY due the space limitation of Google Drive.
Any who uses this code must cite the following bibtex：
@article{ma2019AURCNN,
title = "AU R-CNN: Encoding expert prior knowledge into R-CNN for action unit detection",
journal = "Neurocomputing",
volume = "355",
pages = "35 - 47",
year = "2019",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2019.03.082",
url = "http://www.sciencedirect.com/science/article/pii/S0925231219305338",
author = "Chen Ma and Li Chen and Junhai Yong",
keywords = "Action unit detection, Expert prior knowledge, R-CNN, Facial Action Coding System",
abstract = "Detecting action units (AUs) on human faces is challenging because various AUs make subtle facial appearance change over various regions at different scales. Current works have attempted to recognize AUs by emphasizing important regions. However, the incorporation of expert prior knowledge into region definition remains under-exploited, and current AU detection approaches do not use regional convolutional neural networks (R-CNN) with expert prior knowledge to directly focus on AU-related regions adaptively. By incorporating expert prior knowledge, we propose a novel R-CNN based model named AU R-CNN. The proposed solution offers two main contributions: (1) AU R-CNN directly observes different facial regions, where various AUs are located. Expert prior knowledge is encoded in the region and the RoI-level label definition. This design produces considerably better detection performance than existing approaches. (2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models. Experiment results demonstrate that only static RGB image information and no optical flow-based AU R-CNN surpasses the one fused with dynamic models. AU R-CNN is also superior to traditional CNNs that use the same backbone on varying image resolutions. State-of-the-art recognition performance of AU detection is achieved. The complete network is end-to-end trainable. Experiments on BP4D and DISFA datasets show the effectiveness of our approach. Code will be made available."
}
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