27 lines
1.8 KiB
Plaintext
27 lines
1.8 KiB
Plaintext
The instructions are for a Windows machine, but it should be fairly easy to adapt them to other operating systems
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0. You need to acquire the following AU datasets for the code to work (if you do not have some of them you will need to modify the code slightly to account for that):
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- Bosphorus (http://bosphorus.ee.boun.edu.tr/)
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- BP4D from FERA2015 (http://sspnet.eu/fera2015/)
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- DISFA (http://www.engr.du.edu/mmahoor/DISFA.htm)
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- FERA2011 (http://sspnet.eu/fera2011/fera2011data/)
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- SEMAINE from FERA2015 (http://sspnet.eu/fera2015/)
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- UNBC (http://www.pitt.edu/~emotion/um-spread.htm)
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1. First compile the 64-bit C++ models to be able to extract the features (possible to use 32-bit models as well but default path assumes 64 bit)
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2. Extract features using scripts in the "data extraction" folder, this allows to build a PCA subspace for HOGs that is relevant to faces and facial expressions
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- change the directory locations to where you downloaded the datasets from step 0
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- run all of the extract_features_*.m scripts
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- this will take some time: 4-20 hours depending on your machine as you will need to process a lot of visual data
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- you need to make sure you have the right datasets and they are in the right locations
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3. Create a PCA model
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- run the createSubFaceModels.m in the pca_generation folder
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- this will create a full, lower and upper face PCAs
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- this will take some time (30-60 minutes or so) and require lots of RAM (up to 16GB)
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4. Training the actual models, go to experiments/full_model_training
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- train_static_regressors.m
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- train_static_classifiers.m
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- train_dynamic_regressors.m
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- train_dynamic_classifiers.m
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- train_static_regressors_with_classifiers.m
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- train_dynamic_regressors_with_classifiers.m
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- this will create the .dat files that can be used with the C++ code for AU prediction |