TY - GEN
T1 - LBP with six intersection points
T2 - 12th Asian Conference on Computer Vision 2014
AU - Wang, Yandan
AU - See, John
AU - Phan, Raphael C. W.
AU - Oh, Yee-Hui
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84938850191&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16865-4_34
DO - 10.1007/978-3-319-16865-4_34
M3 - Conference contribution
AN - SCOPUS:84938850191
SN - 9783319168647
T3 - Lecture Notes in Computer Science
SP - 525
EP - 537
BT - Computer Vision – ACCV 2014. ACCV 2014
A2 - Cremers, Daniel
A2 - Reid, Ian
A2 - Saito, Hideo
A2 - Yang, Ming-Hsuan
PB - Springer
Y2 - 1 November 2014 through 5 November 2014
ER -