Abstract
In this paper, a novel feature extraction method called Gabor-based
tensor local discriminant embedding (GTLDE) is proposed. GTLDE first
gets the high-order statistic information by using a biologically
inspired hierarchical model, and then tensor local discriminant
embedding (TLDE) is carried out to extract the discriminant features of
the image for recognition task. The method we proposed is not only
robust to local translation and scale variations, but also has high
distinguishing ability. More importantly, our method can achieve high
accuracy with a small number of training samples. Experimental results
on PolyU-II palmprint database demonstrate the effectiveness of the
method we proposed.
Original language | English |
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Pages (from-to) | 327-342 |
Number of pages | 16 |
Journal | International Journal of Wavelets, Multiresolution and Information Processing |
Volume | 8 |
Issue number | 2 |
DOIs | |
Publication status | Published - Mar 2010 |
Scopus Subject Areas
- Signal Processing
- Information Systems
- Applied Mathematics
User-Defined Keywords
- Dimensionality reduction
- Gabor filter
- Tensor