Abstract
Facial micro-expressions are visible only for a short amount of time so they are not visible to the naked eye, to make this possible we are combining the advancements in video magnification algorithms, which reveal subtle temporal fluctuations in video frames that are difficult to see with the naked eye, and state of the art facial micro-expression recognition neural network models, to enhance the accuracy of facial emotion detection for sentiment analysis. The objective of this project is to detect any minute changes in the video and detect human expressions accurately. Traditional models are sensitive to the intensity of the facial movements captured, and fail to detect the subtle expressions. In this paper, we propose to increase the precision of microexpression recognition to perceive facial movement information of a video frame. The efficiency of our proposed system is based on the CASME & CASME II dataset.
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