LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition

• Published in: Computer Vision -- ACCV 2014 Vol. 9003; pp. 525 - 537
• Main Authors: Wang, Yandan, See, John, Phan, Raphael C.-W., Oh, Yee-Hui
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Extreme Learning Machine; Facial Expression Recognition; Image processing; Local Binary Pattern; Neighbour Point; Orthogonal Plane; Pattern recognition
• ISBN: 3319168649; 9783319168647
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16865-4_34

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.