TY - GEN
T1 - A robust method for near infrared face recognition based on extended local binary pattern
AU - Huang, Di
AU - Wang, Yunhong
AU - Wang, Yiding
PY - 2007
Y1 - 2007
N2 - Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature still suffer from some problems which prevent the further development in face recognition. This paper presents a novel robust method for face recognition under near infrared (NIR) lighting condition based on Extended Local Binary Pattern (ELBP), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of ambient lighting, and ELBP can extract adequate texture features form the NIR images. By combining the local feature vectors, a global feature vector is formed and as the global feature vectors extracted by ELBP operator often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative features for better performance and dimensionality reduction. Compared with the huge number of features produced by ELBP operator, only a small part of the features are selected in this paper, which saves much computation and time cost. The comparison with the results of classic algorithms proves the effectiveness of the proposed method.
AB - Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature still suffer from some problems which prevent the further development in face recognition. This paper presents a novel robust method for face recognition under near infrared (NIR) lighting condition based on Extended Local Binary Pattern (ELBP), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of ambient lighting, and ELBP can extract adequate texture features form the NIR images. By combining the local feature vectors, a global feature vector is formed and as the global feature vectors extracted by ELBP operator often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative features for better performance and dimensionality reduction. Compared with the huge number of features produced by ELBP operator, only a small part of the features are selected in this paper, which saves much computation and time cost. The comparison with the results of classic algorithms proves the effectiveness of the proposed method.
KW - AdaBoost
KW - Extended Local Binary Pattern (ELBP)
KW - Face recognition
KW - Global feature vector
KW - Local feature vector
KW - Near Infrared (NIR)
UR - https://www.scopus.com/pages/publications/38349065414
U2 - 10.1007/978-3-540-76856-2_43
DO - 10.1007/978-3-540-76856-2_43
M3 - 会议稿件
AN - SCOPUS:38349065414
SN - 9783540768555
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 437
EP - 446
BT - Advances in Visual Computing - Third International Symposium, ISVC 2007, Proceedings
PB - Springer Verlag
T2 - 3rd International Symposium on Visual Computing, ISVC 2007
Y2 - 26 November 2007 through 28 November 2007
ER -