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Human facial skin detection in thermal video to effectively measure electrodermal activity (EDA)

In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity (EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras can passively and remotely obtain an analog to EDA by assessing the activation of facial eccrine skin pores. This paper investigates a method to distinguish facial skin from non-skin portions on the face to generate a skin-only Dynamic Mask (DM), validates the DM results, and demonstrates DM performance by removing false pore counts. Moreover, this paper shows results from these techniques using data from 20+ subjects across two different experiments. In the first experiment, subjects were presented with primary screening questions for which some had jeopardy. In the second experiment, subjects experienced standard emotion-eliciting stimuli. The results from using this technique will be shown in relation to data and human perception (ground truth). This paper introduces an automatic end-to-end skin detection approach based on texture feature vectors. In doing so, the paper contributes not only a new capability of tracking facial skin in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for determining autonomic nervous system responses for medical and security applications.