Closed ShirleyYim closed 7 years ago
Hi,
Your question does not have enough details for me to answer it. In our experiments we found that it is more reliable to recognize some facial expressions from HOG features rather than AUs. So I would start from there rather than necesserilly using AUs directly.
Thanks, Tadas
Hi, Do you mean that it is more reliable to use HOG features and SVM to directly predict the emotions such as happy/sad? Thanks for your answer.
Yes exactly. At least experimentally we found that to be more accurate for categorical emotions.
Thanks, Tadas
Hi, I trained a svm model using hog features of about 200 facial expression images, and the detection accuracy is very low. Is it because the training dataset too small ? Thanks, Shirley Yim
It depends on the complexity of the dataset you used, for some it might be enough for others not necessarily. Furthermore, you need to be careful about feature normalization and might possibly need some dimensionality reduction of the HOG features.
Thanks, Tadas
Hi , What set of outputs, are the HOGs? I mean which columns of 709 are related to HOG?
Thanks
HOG are not in the CSV file, but rather in a separate .hog file
Perfect, Thank you,
@TadasBaltrusaitis can you please provide instruction how to visualize the .hog file for each video segment? How can I layout the .hog file for each aligned frame of a short video?
HOG feature visualization is currently done only during the processing of the data, so if you process the original files with -verbose
setting you will see the visualization. You can find the code for it in: Visualise_FHOG
function in VisualizationUtils.cpp
Hi, I use the Feature Extraction result to predict the facial expression, but I can only detect happy and sad. Could you please give me some advice about how to use the SVM and SVR results together? Thank you very much.