@inproceedings{f8f9befc9c784baa9ee698a0a9224ad0,
title = "Subspace KDA algorithm for non-linear feature extraction in face identification",
abstract = "Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results.",
keywords = "Face identification, Kernel discriminant analysis, Shannon wavelet",
author = "Chen, {Wen Sheng} and YUEN, {Pong Chi} and Lai, {Jian Huang}",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Computational Intelligence and Security, CIS 2006 ; Conference date: 03-11-2006 Through 06-11-2006",
year = "2007",
doi = "10.1007/978-3-540-74377-4_116",
language = "English",
isbn = "9783540743767",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1106--1114",
booktitle = "Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers",
address = "Germany",
}