Finally, the feature curves were passed to a LSTM model to predict whether micro-expression occurs. Specifically, we use a sliding window with fixed-length to split a long video into several short videos, then a new and improved optical flow algorithm with low computational complexity was developed to extract feature curves based on the Facial Action Coding System (FACS). This method takes only one step of data preprocessing which is less than previous work. In this paper, we propose a real-time micro-expression detection method based on optical flow and Long Short-term Memory (LSTM) to detect the appearance of micro-expression. Previous work in micro-expression detection mainly focus on finding the peak frame from a video sequence that has been determined to have a micro-expression, and the amount of computation is usually very large. Micro-expressions are momentary involuntary facial expressions which may expose a person’s true emotions.
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