In recent years, research on human emotion estimation using thermal infrared (IR) imagery has appealed to many
researchers due to its invariance to visible illumination changes. Although infrared imagery is superior to visible imagery
in its invariance to illumination changes and appearance differences, it has difficulties in handling transparent glasses in
the thermal infrared spectrum. As a result, when using infrared imagery for the analysis of human facial information, the
regions of eyeglasses are dark and eyes’ thermal information is not given. We propose a temperature space method to
correct eyeglasses’ effect using the thermal facial information in the neighboring facial regions, and then use Principal
Component Analysis (PCA), Eigen-space Method based on class-features (EMC), and PCA-EMC method to classify
human emotions from the corrected thermal images. We collected the Kotani Thermal Facial Emotion (KTFE) database
and performed the experiments, which show the improved accuracy rate in estimating human emotions.
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