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

Yandan Wang, John See, Raphael C. W. Phan, Yee-Hui Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2014. ACCV 2014
EditorsDaniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
PublisherSpringer
Pages525-537
Number of pages13
ISBN (Electronic)9783319168654
ISBN (Print)9783319168647
DOIs
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

Publication series

NameLecture Notes in Computer Science
Volume9003
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Asian Conference on Computer Vision 2014
Abbreviated titleACCV 2014
Country/TerritorySingapore
CitySingapore
Period1/11/145/11/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'LBP with six intersection points: Reducing redundant information in LBP-TOP for micro-expression recognition'. Together they form a unique fingerprint.

Cite this