Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Palmprint Verification: An Implementation of Biometric Technology
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Palmprint recognition using wavelet and support vector machines
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Directional energy based palmprint identification using non subsampled contourlet transform
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel personal authentication system using palmprint technology
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Palmprint recognition based on improved 2DPCA
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Characterization of palmprints by wavelet signatures via directional context modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Palm line extraction and matching for personal authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
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Palmprint based Identification is gaining popularity due to its traits like user acceptance, reliability and ease of acquisition. The paper presents a recognition method which extorts textural information obtainable from the palmprint, utilizing different filters of wavelet transform. Palmprint center has been computed using the chessboard metric of Distance Transform whereas the structures of best fitting ellipse help resolve the alignment of the palmprint. Region Of Interest of 256×256 pixels is clipped around the center. Next, normalized directional energy components of the decomposed subband outputs are computed for each block. Biorthogonal, Symlet, Discrete Meyer, Coiflet, Daubechies and Mexican hat wavelets are investigated on 500 palmprints acquired from 50 users with 10 samples each for their individual and concatenated combined features vectors. The performance has been analyzed using Euclidean classifier. An Equal Error Rate (EER) of 0.0217 and Genuine Acceptance Rate (GAR) of 97.12% with combined feature vector formed by Bior3.9, Sym8 and Dmeyer wavelets depict better performance over individual wavelet transforms and combination of coiflet, Daubechies and Mexican hat wavelets.