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Multimedia Tools and Applications

, Volume 75, Issue 10, pp 5777–5794 | Cite as

Noise and rotation invariant RDF descriptor for palmprint identification

  • Deepti Tamrakar
  • Pritee Khanna
Article

Abstract

Rotation and noise invariant feature extraction is a challenge in palmprint recognition. This work presents a novel RDF descriptor based on Radon, Dual tree complex wavelet, and Fourier transforms. Combined properties of these transforms help to explore efficiency and robustness of RDF descriptor for palmprint identification. Radon transform can capture directional features of the palmprint and is robust to additive white Gaussian noise also. It converts rotation into translation. 1D Dual tree complex wavelet transform (DTCWT) applied on Radon coefficients in angle direction removes translation in Radon coefficients due to palmprint rotation. The magnitude of 2D Fourier transform performed on resultant coefficients helps to extract rotation and illumination invariant features. The performance of the proposed RDF descriptor is evaluated on noisy and rotated palmprints upto 10. Trained with normal palmprints only, the proposed system gives good results for rotated and noisy palmprints. Experiments are performed on PolyU 2D, CASIA, and IIITDMJ databases. Theoretical foundations and experimental results show the robustness of RDF descriptor against additive white noise and rotation.

Keywords

Radon transform Dual tree complex wavelet transform Fourier transform Euclidean distance Gaussian noise 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing JabalpurJabalpurIndia

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