A novel one-parameter regularized linear discriminant analysis for solving small sample size problem in face recognition

Wensheng Chen*, Pong Chi YUEN, Jian Huang, Daoqing Dai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, a new 1-parameter regularized discriminant analysis (IPRDA) algorithm is developed to deal with the small sample size (S3) problem. The main limitation in regularization is that the computational complexity of determining the optimal parameters is very high. In view of this limitation, we derive a single parameter (t) explicit expression formula for determining the 3 parameters. A simple and efficient method is proposed to determine the value of t. The proposed IPRLDA method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. The average recognition accuracy of 50 runs for ORL and FERET database are 96.65% and 94.00% respectively. Comparing with existing LDA-based methods in solving the S3 problem, the proposed 1PRLDA method gives the best performance.

Original languageEnglish
Pages (from-to)320-329
Number of pages10
JournalLecture Notes in Computer Science
Volume3338
DOIs
Publication statusPublished - 2004

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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