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

Authors:
Wensheng Chen;Pong C Yuen;Jian Huang;Daoqing Dai
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, Zhongshan University, P.R China
Venue:
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Year:
2004

Citing 9
Cited 0

Using Discriminant Eigenfeatures for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition System Using Local Autocorrelations and Multiscale Integration

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Wavelet-based 2-parameter regularized discriminant analysis for face recognition

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition using kernel direct discriminant analysis algorithms

IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.