Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A study of identical twins' palmprints for personal verification
Pattern Recognition
Orthogonal Moments Based Texture Analysis of CT Liver Images
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Contourlet Spectral Histogram for Texture Classification
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
An Authentication System Based on Palmprint
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
The Contourlet Transform with the Principal Component Analysis for Palmprint Recognition
CICSYN '10 Proceedings of the 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
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In recent usage of internet in financial and information transaction, authentication becomes necessity for authorized access. Palmprint recognition is a widely accepted biometric authentication among various biometrics methodologies. The enormous feature content and less acquisition cost make it more reliable and user friendly. Texture is one of the vital features in biometric recognition applications. Though many statistical methods are available to extract the texture, non-subsampled contourlet transform is employed in this work as a primary step to extract the directional frequency information content in the palmprint, followed by the statistical moment extraction. In addition to using the Zernike moments as texture descriptors, they are effectively used in reducing the dimensionality of contourlet coefficients. Since Zernike moments are inherently orthogonal and rotation invariant, they are more suitable for palmprint recognition.