Micro-expressions recognition using center symmetric local mapped pattern
Kam Meng Goh, Li Li Lim · 2019
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.