TY - GEN
T1 - Face recognition based on LBP and extreme learning machine
AU - Wu, Jing Hui
AU - Han, Lu
AU - Li, Jia Tong
AU - Zhao, Yi Xiao
AU - Tang, Lin Bo
AU - Zhao, Bao Jun
PY - 2013
Y1 - 2013
N2 - This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.
AB - This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.
KW - Extreme learning machine
KW - Human face recognition
KW - Local binary pattern
KW - Support vector machine
KW - Texture features
UR - http://www.scopus.com/inward/record.url?scp=84884825610&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.380-384.3526
DO - 10.4028/www.scientific.net/AMM.380-384.3526
M3 - Conference contribution
AN - SCOPUS:84884825610
SN - 9783037858202
T3 - Applied Mechanics and Materials
SP - 3526
EP - 3529
BT - Vehicle, Mechatronics and Information Technologies
T2 - 2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013
Y2 - 17 August 2013 through 18 August 2013
ER -