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.
Scale-Invariant Feature Transform(SIFT) and Speeded-Up Robust Feature(SURF) are common techniques used for
extracting robust features that can be used to perform matching between different viewpoints of scenes. Both methods
basically involve three main stages, which are feature extraction, orientation assignment and feature descriptor extraction
for matching. SURF is computation efficient compared to SIFT because the integral image is used for the convolutions
to reduce computation time. However, both methods also do not focus much on the technique of matching. This paper
introduces a method which can help to improve the rotational matching performance in term of accuracy by establishing
a decision matrix and an approximated rotational angle within two corresponding images. The proposed method
generally improved the matching rate around 10% to 20% in terms of accuracy.
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