Abstract
Micro-expressions are brief involuntary facial expressions. Detecting micro-expressions consists of finding the occurrence of micro-expressions in video sequences by locating the onset, peak and offset frames. This paper proposes an algorithm to detect micro-expressions by utilizing the motion features to capture direction continuity. It computes the optical flow vector for small local spatial regions and integrates them in local spatiotemporal regions. It uses heuristics to filter non-micro expressions and find the appropriate onset and offset times. Promising results are obtained on a challenging spontaneous micro-expression database. The main contribution of this paper is to find not only the peak but also the onset and offset frames for spotted micro-expressions which has not been explored before.
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Patel, D., Zhao, G., Pietikäinen, M. (2015). Spatiotemporal Integration of Optical Flow Vectors for Micro-expression Detection. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_32
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DOI: https://doi.org/10.1007/978-3-319-25903-1_32
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