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Lecture Notes in Computer Science
This paper describes the challenges of getting ground truth affective labels for spontaneous video, and presents implications for systems such as virtual agents that have automated facial analysis capabilities. We first present a dataset from an intelligent tutoring application and describe the most prevalent approach to labeling such data. We then present an alternative labeling approach, which closely models how the majority of automated facial analysis systems are designed. We show thatdoi:10.1007/978-3-642-04380-2_37 fatcat:2p6y72g3cjglfcxjcn54b4iwgi