Dual unsupervised discriminant projection for face recognition

Authors:
Lei Tang;Jie Gui
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China and Department of Automation, University of Science and Technology of China, He ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China and Department of Automation, University of Science and Technology of China, He ...
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

Citing 4
Cited 0

Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

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
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we proposed a dual unsupervised discriminant projection (DUDP) method for dimensionality reduction tasks. The proposed method is derived from the efficient unsupervised method called unsupervised discriminant projection (UDP). UDP takes into account both the local and nonlocal characteristics to seek a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. While UDP adopt PCA procedure to avoid a singular scatter matrix by ruling out some small principal components in which it lost a lot of potential and valuable discriminant information of original data. To overcome this problem, we proposed our algorithm to carry out discriminant analysis both in null space and range space to avoid loss of discriminant information. The advantage of our algorithm is borne out by comparison with some other widely used methods in the experiments on Yale face database.