A Novel One-Parameter Regularized Linear Discriminant Analysis for Solving Small Sample Size Problem in Face Recognition

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• 作者：Wensheng Chen ; Pong C Yuen ; Jian Huang ; Daoqing Dai
• 刊名：Lecture Notes in Computer Science
• 出版年：2004
• 出版时间：2004
• 年：2004
• 卷：3338
• 期：1
• 全文大小：422 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper, a new 1-parameter regularized discriminant analysis (1PRDA) 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 1PRLDA 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.