TY - GEN
T1 - Interpolatory Mercer kernel construction for kernel direct LDA on face recognition
AU - Chen, Wen Sheng
AU - Yuen, Pong C.
N1 - Funding information:
This project is supported by the Hong Kong RGC General Research Fund HKBU2113/06E and NSF of China (60873168). The authors would like to thank for the US Army Research Laboratory for contribution of the FERET database and CMU for the CMU PIE database
PY - 2009
Y1 - 2009
N2 - This paper proposes a novel methodology on Mercer kernel construction using interpolatory strategy. Based on a given symmetric and positive semi-definite matrix (Gram matrix) and Cholesky decomposition, it first constructs a nonlinear mapping Φ, which is well-defined on the training data. This mapping is then extended to the whole input feature space by utilizing Lagrange interpolatory basis functions. The kernel function constructed by inner product is proven to be a Mercer kernel function. The self-constructed interpolatory Mercer (IM) kernel keeps the Gram matrix unchanged on the training samples. To evaluate the performance of the proposed IM kernel, a popular kernel direct linear discriminant analysis (KDDA) method for face recognition is selected. Comparing with RBF kernel based KDDA method on two face databases, namely FERET and CMU PIE databases, the IMkernel based KDDA approach could increase the performance by around 20% on CMU PIE database.
AB - This paper proposes a novel methodology on Mercer kernel construction using interpolatory strategy. Based on a given symmetric and positive semi-definite matrix (Gram matrix) and Cholesky decomposition, it first constructs a nonlinear mapping Φ, which is well-defined on the training data. This mapping is then extended to the whole input feature space by utilizing Lagrange interpolatory basis functions. The kernel function constructed by inner product is proven to be a Mercer kernel function. The self-constructed interpolatory Mercer (IM) kernel keeps the Gram matrix unchanged on the training samples. To evaluate the performance of the proposed IM kernel, a popular kernel direct linear discriminant analysis (KDDA) method for face recognition is selected. Comparing with RBF kernel based KDDA method on two face databases, namely FERET and CMU PIE databases, the IMkernel based KDDA approach could increase the performance by around 20% on CMU PIE database.
KW - Face recognition
KW - KDDA
KW - Mercer kernel
UR - http://www.scopus.com/inward/record.url?scp=70349197757&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4959719
DO - 10.1109/ICASSP.2009.4959719
M3 - Conference proceeding
AN - SCOPUS:70349197757
SN - 9781424423545
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 857
EP - 860
BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Y2 - 19 April 2009 through 24 April 2009
ER -