[PDF][PDF] Automated facial emotion recognition: Development and application to human-robot interaction

X Liu - 2019 - etd.ohiolink.edu
2019etd.ohiolink.edu
3.1 AUs, FSs with description, and associated landmarks for each FS grouped into five
categories........................ 24 3.2 Analysis of LC and VL features and polynomial interpolation
order. 32 3.3 LC-VL Feature Improvements based on AU Combinations for CK+. 33 3.4 LC-
VL Feature Improvements based on AU Combinations for MUG. 33 3.5 CK+ dataset
Classification results for seven and six emotions denoted as A (Anger), D (Disgust), F (Fear),
H (Happiness), Su (Surprise), S (Sadness) and N (Neutral) with accuracy in%............. 36 3.6 …
3.1 AUs, FSs with description, and associated landmarks for each FS grouped into five categories........................ 24 3.2 Analysis of LC and VL features and polynomial interpolation order. 32 3.3 LC-VL Feature Improvements based on AU Combinations for CK+. 33 3.4 LC-VL Feature Improvements based on AU Combinations for MUG. 33 3.5 CK+ dataset Classification results for seven and six emotions denoted as A (Anger), D (Disgust), F (Fear), H (Happiness), Su (Surprise), S (Sadness) and N (Neutral) with accuracy in%............. 36 3.6 MUG dataset Classification results for seven and six emotions denoted as A (Anger), D (Disgust), F (Fear), H (Happiness), Su (Surprise), S (Sadness) and N (Neutral) with accuracy in%............. 36 iv
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