LBP with six intersection points: Reducing redundant information in LBP-TOP for micro-expression recognition

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 leaveone-sample-out cross validation, we achieve the best recognition accuracy of 67.21 %, surpassing the baseline performance with further computational efficiency.