Corpus ID: 212453178

Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network

@inproceedings{Abukmeil2014PalmprintRB,
  title={Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network},
  author={Mohanad Abukmeil and Hatem Ali Elaydi and Mohammed Alhanjouri},
  year={2014}
}
Palmprint recognition has emerged as a substantial biometric based personal identification. Tow types of biometrics palmprint feature. high resolution feature that includes: minutia points, ridges and singular points that could be extracted for forensic applications. Moreover, low resolution feature such as wrinkles and principal lines which could be extracted for commercial applications. This paper uses 700nm spectral band PolyU hyperspectral palmprint database. Multiscale image transform… Expand

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References

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Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network
TLDR
This paper uses PolyU hyperspectral palmprint database, and applies back-propagation neural network for recognition, linear discriminate analysis for dimensionality reduction, and 2D discrete wavelet, ridgelet, curvelet, and contourlet for feature extraction. Expand
Palmprint Recognition Using Wavelet Decomposition and 2D Principal Component Analysis
TLDR
A novel method using wavelet decomposition and 2D Principal component analysis (2DPCA) for palmprint recognition achieves comparatively high recognition accuracy and more computationally efficient than using other feature extraction techniques such as principal component analysis and independent component analysis. Expand
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TLDR
In this paper two techniques for palmprint recognition are suggested and compared and it was found that the best achieved recognition rate is about 94% when combining the results of both techniques using the CT. Expand
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TLDR
Preliminary studies on feature band selection by analyzing hyper spectral palm print data showed that 2 spectral bands at 700nm and 960nm could provide most discriminate information of palm print, which could be used for designing multispectral palm print systems in the future. Expand
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A Hybrid wavelet, generated by using Kronecker product of two existing orthogonal transforms, Walsh and DCT to identify multi-spectral palmprints, can significantly improve the identification rates for palmprint images. Expand
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TLDR
The system consists of a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition, and a robust image coordinate system is defined to facilitate image alignment for feature extraction. Expand
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