DT-CWT Feature Combined with ONPP for Face Recognition

Authors:
Yuehui Sun;Minghui Du
Affiliations:
School of Electronic & Information Engineering, South China University, of Technology, Guangzhou, 510640, China;School of Electronic & Information Engineering, South China University, of Technology, Guangzhou, 510640, China
Venue:
Computational Intelligence and Security
Year:
2007

Citing 3
Cited 1

Orthogonal Neighborhood Preserving Projections

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Shift invariant properties of the dual-tree complex wavelet transform

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

IEEE Transactions on Image Processing

An approach for face recognition based on fusion of DTCWT and manifold regularized orthogonal discriminant analysis

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel face recognition method based on DT-CWT feature representation using ONPP. The Dual-Tree Complex Wavelet Transform (DT-CWT) used for representation features of face images, whose kernels are similar to Gabor wavelets, exhibit desirable characteristics of spatial locality and orientation selectivity. And DT-CWT outperforms Gabor with less redundancy and much efficient computing. Orthogonal Neighborhood Preserving Projections (ONPP) is a linear dimensionality reduction technique which attempts to preserve both the intrinsic neighborhood geometry of the data samples and the global geometry. ONPP employs an explicit linear mapping between the two. As a result, ONPP can handle new data samples straightforward, as this amount to a simple linear transformation. The experimental results have demonstrated the advantageous characteristics of ONPP in the DT-CWT feature space and achieve the better face recognition performance.