A novel regularized fisher discriminant method for face recognition based on subspace and rank lifting scheme

Authors:
Wen-Sheng Chen;Pong Chi Yuen;Jian Huang;Jianhuang Lai;Jianliang Tang
Affiliations:
Department of Mathematics, Shenzhen University, P.R. China;Department of Computer Science, Hong Kong Baptist University, Hong Kong;Department of Computer Science, Hong Kong Baptist University, Hong Kong;Department of Mathematics, Sun Yat-Sen University, P.R. China;Department of Mathematics, Shenzhen University, P.R. China
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 5
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using LDA-based algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The null space N(St) of total scatter matrix St contains no useful information for pattern classification. So, discarding the null space N(St) results in dimensionality reduction without loss discriminant power. Combining this subspace technique with proposed rank lifting scheme, a new regularized Fisher discriminant (SL-RFD) method is developed to deal with the small sample size (S3) problem in face recognition. Two public available databases, namely FERET and CMU PIE databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed SL-RFD method gives the best performance.