Abstract
Hyperspectral imaging technology (HSI) can be used to remotely detect emotion levels in humans based on the tissue oxygen saturation (StO2) from their faces. Our prior research demonstrated some significant differences among the variations of StO2 in different facial regions of interest (ROIs) when the subjects are truly happy. Experiment is conducted for data collection with HSI where the subjects (a total of fifty participants, who rigidly conform to the research protocol) are required to show spontaneous emotions (calm, happy, and unhappy: angry). Based on the comprehensive analysis of musculoskeletal anatomy and facial action coding system (FACS), 19 ROIs have been determined. Consequently, a 19 dimensional StO2 vector is constructed as a feature input instead of employing the entire facial region. Lastly, experimental results through basic K-Nearest Neighbor (KNN) report an average accuracy rate of 83.32% when only considering fewer StO2 features. In addition, some experiments using other advanced machine learning methods also validate its effective recognition performance with facial StO2 as an affective indicator. Our findings demonstrate the possible practical feasibility of StO2 in happiness detection.
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