Abstract—Due to the proliferation of internet usage in financial and information transaction, authentication becomes mandatory for authorised access. Palmprint recognition is a widely accepted biometric authentication. Richness of feature and the less cost involved in acquisition 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 first step to extract the directional frequency information 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.
Index Terms—Biometrics, palmprint, ROI extraction, feature extraction, nonsubsampled contourlet transform, zernike moments.
M. A. Leo Vijilious is with the Sathyabama University, Chennai (e-mail:email@example.com)
V. Subbiah Bharathi is with the DMI College of Engineering, Chennai(e-mail: firstname.lastname@example.org)
Cite: M. A Leo Vijilious and V. Subbiah Bharathi, "Texture Feature Extraction Approach to Palmprint using Nonsubsampled Contourlet Transform and Orthogonal Moments," International Journal of Future Computer and Communication vol. 1, no. 3, pp. 298-301, 2012.