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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References
de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP \(-\) TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013)
Ekman, P.: Lie catching and microexpressions. In: Martin, C.W. (ed.) The Philosophy of Deception, pp. 118–133. Oxford University Press, Oxford (2009)
Kellokumpu, V., Zhao, G., Li, S.Z., Pietikäinen, M.: Dynamic texture based gait recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1000–1009. Springer, Heidelberg (2009)
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikainen, M.: A spontaneous microexpression database: inducement, collection and baseline. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6 (2013)
Mattivi, R., Shao, L.: Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 740–747. Springer, Heidelberg (2009)
Qian, X., Hua, X.S., Chen, P., Ke, L.: PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recogn. 44(10), 2502–2515 (2011)
Shan, C., Gritti, T.: Learning discriminative LBP-histogram bins for facial expression recognition. In: BMVC, pp. 1–10 (2008)
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
Wang, S.J., Chen, H.L., Yan, W.J., Chen, Y.H., Fu, X.: Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process. Lett. 39(1), 25–43 (2014)
Yan, W.J., Wu, Q., Liu, Y.J., Wang, S.J., Fu, X.: CASME database: a dataset of spontaneous micro-expressions collected from neutralized faces. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–7 (2013)
Yan, W.J., Wang, S.J., Liu, Y.J., Wu, Q., Fu, X.: For micro-expression recognition: database and suggestions. Neurocomputing 136, 82–87 (2014)
Yan, W.J., Li, X., Wang, S.J., Zhao, G., Liu, Y.J., Chen, Y.H., Fu, X.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PloS One 9(1), e86041 (2014)
Zhao, G., Pietikainen, M.: Local binary pattern descriptors for dynamic texture recognition. In: 18th International Conference on Pattern Recognition (ICPR), vol. 2, pp. 211–214. IEEE (2006)
Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Zhao, G., Pietikainen, M.: Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern Recogn. Lett. 30(12), 1117–1127 (2009)
Zhao, G., Ahonen, T., Matas, J., Pietikainen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process. 21(4), 1465–1477 (2012)
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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