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Proceedings Paper

Micro-expressions recognition using center symmetric local mapped pattern
Author(s): Kam Meng Goh; Li Li Lim
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Paper Abstract

Local feature description is widely used in micro-expressions (ME) recognition. However, contemporary low-level handcrafted feature is insufficient in representing ME due to its insignificant and subtle motion which results in low recognition rate. This paper presents a novel handcrafted feature to represent ME based on intensity-level difference mapping, namely Center-Symmetric Local Mapped Pattern (CS-LMP). Due to its capability in capturing subtle pixel changes, CS-LMP is proposed to retrieve ME subtle motions which results in better accuracy. In this paper, CS-LMP features are extracted from ME public datasets and the results are compared to other state-of-the-art approaches where the classifications are performed using support vector machine and K-nearest neighbours. The results show that our approach produces prominent results as high as 79.59% compared to competing approaches.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411O (15 March 2019); doi: 10.1117/12.2522843
Show Author Affiliations
Kam Meng Goh, Univ. Tunku Abdul Rahman (UTAR) (Malaysia)
Li Li Lim, Univ. Tunku Abdul Rahman (UTAR) (Malaysia)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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