Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition
Pattern Recognition Letters
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
A survey of palmprint recognition
Pattern Recognition
Handprint Recognition: A Novel Biometric Technology
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Invariant pattern recognition using contourlets and AdaBoost
Pattern Recognition
Palmprint recognition based on unsupervised subspace analysis
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Palmprint classification using wavelets and adaboost
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A palmprint classification method based on finite ridgelet transformation and SVM
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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In this paper, we present a new feature extraction method for palmprint recognition The digital curvelet transform is revised here and used to extract the palmprint features In our algorithm, we use the discrete Meyer wavelet transform to replace the “à trous” transform, then apply the ridgelet transform to each block which is subbanded after the discrete Meyer wavelet transform from the palmprint image Our work is carried on the PolyU Palmprint Database Dealing with the palmprint image sized of 64 × 64, our new strategy acquires 4 × 128 × 128 curvelet coefficients Based on the system performance, the best coefficients threshold can be obtained With this threshold the curvelet coefficients are filtered and less than 2% of coefficients are selected With this compressed coefficients set, the correct recognition rate of our palmprint identification experiment is up to 95.25%.