TY - GEN
T1 - Learning multi-scale block local binary patterns for face recognition
AU - Liao, Shengcai
AU - Zhu, Xiangxin
AU - Lei, Zhen
AU - Zhang, Lun
AU - Li, Stan Z.
PY - 2007
Y1 - 2007
N2 - In this paper, we propose a novel representation, called Multi-scale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.
AB - In this paper, we propose a novel representation, called Multi-scale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.
KW - AdaBoost
KW - Face recognition
KW - LBP
KW - MB-LBP
UR - http://www.scopus.com/inward/record.url?scp=37849037421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37849037421&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74549-5_87
DO - 10.1007/978-3-540-74549-5_87
M3 - Conference contribution
AN - SCOPUS:37849037421
SN - 9783540745488
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 828
EP - 837
BT - Advances in Biometrics - International Conference, ICB 2007, Proceedings
PB - Springer Verlag
T2 - 2007 International Conference on Advances in Biometrics, ICB 2007
Y2 - 27 August 2007 through 29 August 2007
ER -