TY - JOUR
T1 - Extracting multiple features in the CID color space for face recognition
AU - Liu, Zhiming
AU - Yang, Jian
AU - Liu, Chengjun
N1 - Funding Information:
Manuscript received April 01, 2009; revised September 23, 2009; accepted March 16, 2010. First published April 22, 2010; current version published August 18, 2010. The work of J. Yang is supported in part by the Program for New Century Excellent Talents in University of China, in part by the NUST Outstanding Scholar Supporting Program, and in part by the National Science Foundation of China under Grants no. 60973098 and 60632050. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ercan E. Kuruoglu.
PY - 2010/9
Y1 - 2010/9
N2 - This correspondence presents a novel face recognition method that extracts multiple features in the color image discriminant (CID) color space, where three new color component images, D1, D2, and D3, are derived using an iterative algorithm. As different color component images in the CID color space display different characteristics, three different image encoding methods are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Experimental results using two large-scale face databases, namely, the face recognition grand challenge (FRGC) version 2 database and the FERET database, show the effectiveness of the proposed method.
AB - This correspondence presents a novel face recognition method that extracts multiple features in the color image discriminant (CID) color space, where three new color component images, D1, D2, and D3, are derived using an iterative algorithm. As different color component images in the CID color space display different characteristics, three different image encoding methods are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Experimental results using two large-scale face databases, namely, the face recognition grand challenge (FRGC) version 2 database and the FERET database, show the effectiveness of the proposed method.
KW - Color component images
KW - FERET
KW - color image discriminant (CID) color space
KW - face recognition
KW - face recognition grand challenge (FRGC)
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U2 - 10.1109/TIP.2010.2048963
DO - 10.1109/TIP.2010.2048963
M3 - Article
C2 - 20421188
AN - SCOPUS:77955789861
SN - 1057-7149
VL - 19
SP - 2502
EP - 2509
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 9
M1 - 5452973
ER -