49 lines
3.8 KiB
Plaintext
49 lines
3.8 KiB
Plaintext
--------------------------------------- OpenFace Matlab runners -----------------------------------------------------------------------------
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These are provided for recreation of some of the experiments described in the publications and to demonstrate the command line interface by calling the C++ executables from Matlab.
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======================== Demos ==================================
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run_demo_images.m - running the FaceLandmarkImg landmark detection on the demo images packaged with the code
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run_demo_videos.m - running the FaceTrackingVid landmark detection and tracking on prepackaged demo videos
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run_demo_video_multi.m - running the FaceTrackingVidMulti landmark detection and tracking on prepackaged demo videos (the difference from above is that it can deal with multiple faces)
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For extracting head pose, facial landmarks, HOG features, aligned faces, eye gaze, and Facial Action Units look at the following demos:
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feature_extraction_demo_img_seq.m - Running the FeatureExtraction project, it demonstrates how to specify parameters for extracting a number of features from a sequence of images in a folder and how to read those features into Matlab.
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feature_extraction_demo_vid.m - Running the FeatureExtraction project, it demonstrates how to specify parameters for extracting a number of features from a video and how to read those features into Matlab.
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gaze_extraction_demo_vid.m - Example of a clip with varying gaze and extraction of eye gaze information
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The other scripts are for unit testing of the code:
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- run_demo_align_size.m
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- run_tes_img_seq.m
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The demos are configured to use CLNF patch experts trained on in-the-wild and Multi-PIE datasets, it is possible to uncomment other model file definitions in the scripts to run them instead.
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======================== Head Pose Experiments ============================
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To run them you will need to have the appropriate datasets and to change the dataset locations.
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run_head_pose_tests_OpenFace.m - runs CLNF on the 3 head pose datasets (Boston University, Biwi Kinect, and ICT-3DHP you need to acquire the datasets yourself)
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======================== Feature Point Experiments ============================
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run_OpenFace_feature_point_tests_300W.m runs CLM and CLNF on the in the wild face datasets acquired from http://ibug.doc.ic.ac.uk/resources/300-W/
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The code uses the already defined bounding boxes of faces (these are produced using the 'ExtractBoundingBoxes.m' script on the in the wild datasets). The code relies on there being a .txt file of the same name as the image containing the bounding box in the appropriate directory, see https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments for details.
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To run the code you will need to download the 300-W challenge datasets and then replace the database_root with the dataset location.
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This script also includes code to draw a graph displaying error curves of the CLNF and CLM methods trained on in the wild data.
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For convenient comparisons to other state-of-art approaches it also includes results of using the following approaches on the 300-W datasets:
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run_yt_dataset.m run the CLNF model on the YTCeleb Database (https://sites.google.com/site/akshayasthana/Annotations), you need to get the dataset yourself though.
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======================== Action Unit Experiments ============================
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Evaluating our Facial Action Unit detection system on Bosphorus, BP4D, DISFA, FERA2011 and SEMAINE datasets.
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As the models were partially trained/validated on DISFA, FERA2011, BP4D, UNBC, Bosphorus, and SEMAINE datasets the results might not generalise across datasets. However, this demonstrates how AU prediction can be done with our system.
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======================== Gaze Experiments ============================
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Evaluating our gaze estimation on the MPIIGaze dataset, run the extract_mpii_gaze_test.m script in the Gaze Experiments folder
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