TY - GEN
T1 - Mutual neighborhood based discriminant projection for face recognition
AU - Niu, Ben
AU - Shiu, Chi Keung Simon
AU - Pal, Sankar
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Linear Discriminant Analysis is optimal under the assumption that the covariance matrices of the conditional densities are normal and all identical. However, this doesn't hold for many real world applications, such as Facial Image Recognition, in which data are typically under-sampled and non-Gaussian. To address this deficiency the Non-Parametric Discriminant method has been developed, but it requires model selection to be carried out for selecting the free control parameters, making it not easy for use in practice. We proposed a method, Mutual Neighborhood based Discriminant Projection, to overcome this problem. MNDP identifies the samples that contribute most to the Baysesian errors and highlights them for optimization. It is more convenient for use than NDA and avoids the singularity problem of LDA. On facial image datasets MNDP is shown to outperform Eigenfaces and Fisherfaces under various experimental conditions.
AB - Linear Discriminant Analysis is optimal under the assumption that the covariance matrices of the conditional densities are normal and all identical. However, this doesn't hold for many real world applications, such as Facial Image Recognition, in which data are typically under-sampled and non-Gaussian. To address this deficiency the Non-Parametric Discriminant method has been developed, but it requires model selection to be carried out for selecting the free control parameters, making it not easy for use in practice. We proposed a method, Mutual Neighborhood based Discriminant Projection, to overcome this problem. MNDP identifies the samples that contribute most to the Baysesian errors and highlights them for optimization. It is more convenient for use than NDA and avoids the singularity problem of LDA. On facial image datasets MNDP is shown to outperform Eigenfaces and Fisherfaces under various experimental conditions.
KW - Discriminant projection
KW - Face recognition
KW - K-nearest neighbors
KW - Mutual neighborhood
UR - http://www.scopus.com/inward/record.url?scp=76249123195&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-11164-8_71
DO - 10.1007/978-3-642-11164-8_71
M3 - Conference article published in proceeding or book
SN - 3642111637
SN - 9783642111631
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 440
EP - 445
BT - Pattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings
T2 - 3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009
Y2 - 16 December 2009 through 20 December 2009
ER -