Author : NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors : Yomna M. Elbarawy, Rania Salah El-Sayed
Source : Bulletin of Electrical Engineering and Informatics
Date of Publication : 12/2018
Abstract :
Emotional reactions are the best way to express human attitude and thermal
imaging mainly used to utilize detection of temperature variations as in
detecting spatial and temporal variation in the water status of grapevine. By
merging the two facts this paper presents the Discrete Cosine Transform
(DCT) with Local Entropy (LE) and Local Standard Deviation (LSD)
features as an efficient filters for investigating human emotional state in
thermal images. Two well known classifiers, K-Nearest Neighbor (KNN) and
Support Vector Machine (SVM) were combined with the earlier features and
applied over a database with variant illumination, as well as occlusion by
glasses and poses to generate a recognition model of facial expressions in
thermal images. KNN based on DCT and LE gives the best accuracy
compared with other classifier and features results.
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