Abstract
Facial micro-expression recognition is an upcoming area in computer vision research. Up until the recent emergence of the extensive CASMEII spontaneous micro-expression database, there were numerous obstacles faced in the elicitation and labeling of data involving facial micro-expressions. In this paper, we propose the Local Binary Patterns with Six Intersection Points (LBP-SIP) volumetric descriptor based on the three intersecting lines crossing over the center point. The proposed LBP-SIP reduces the redundancy in LBP-TOP patterns, providing a more compact and lightweight representation; leading to more efficient computational complexity. Furthermore, we also incorporated a Gaussian multi-resolution pyramid to our proposed approach by concatenating the patterns across all pyramid levels. Using an SVM classifier with leave-one-sample-out cross validation, we achieve the best recognition accuracy of 67.21 %, surpassing the baseline performance with further computational efficiency.
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Acknowledgement
We thank the Chinese Academy of Sciences for access to the CASMEII micro-expression database and Su-Jing Wang for providing more details on their CASMEII work [12]. We also thank the anonymous reviewers for their constructive comments. This research work is funded by the TM Grant under project UbeAware.
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Wang, Y., See, J., Phan, R.CW., Oh, YH. (2015). LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_34
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DOI: https://doi.org/10.1007/978-3-319-16865-4_34
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