Edward Kim
ABSTRACT
Although machines are more pervasive in our everyday lives, we are still forced to interact with them through limited communication channels. Our overarching goal is to support new and complex interactions by teaching the computer to interpret the expressions of the user. Towards this goal, we present Vinereactor, a new labeled database for face analysis and affect recognition. Our dataset is one of the first to explore human expression recognition in response to a stimulus video, enabling a new facet of affect analysis research. Furthermore, our dataset is the largest of its kind, nearly a magnitude larger than its closest related work.
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Index Terms
- Vinereactor: Crowdsourced Spontaneous Facial Expression Data
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