Multispectral Palmprint Recognition Based on Multidirectional Transform

Year 2019, Volume: 7 Issue: 2, 162 - 170, 30.04.2019

Burcin Ozmen Olayinka John Olaleye

https://doi.org/10.17694/bajece.518050

Abstract

Multispectral palmprint recognition is one of
the most useful biometric techniques due to features obtained from different
spectral resolutions/wavelengths. In this paper, we propose a multidirectional
transform-based feature encoding plan for reliable and robust representation
and matching of multispectral palm images. The method extracts the region of
interest (ROI) for palmprint images captured with non-contact sensors. The
registered ROI of each band is newly downsampled using DWT. This approach
allows us to take more lines into consideration for interpolation. A
undecimated dual-tree complex wavelet transform based multidirectional feature
encoding plan is then newly applied since it provides better shift invariance
and directional selectivity.  Finally, a
binary code matching strategy with score level fusion is used to compute
matching for efficient identification. The experimental results obtained on
CASIA and PolyU datasets show that the presented method gives better results in
the blurring binary code matching case than state-of-the-art methods and
provides comparable performance in the non-blurring binary code matching. 

Keywords

Feature Extraction, Image Recognition, Matching, Multispectral Encoding, Pattern Analysis, Wavelet Transforms

References

• Reference1
• 6712-1267
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