Verification of biometric palmprint patterns using optimal trade-off filter classifiers

Authors:
Pablo Hennings;Marios Savvides;B. V. K. Vijaya Kumar
Affiliations:
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 6
Cited 1

Matching of palmprints

Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition

Pattern Recognition Letters
Reduced complexity face recognition using advanced correlation filters and fourier subspace methods for biometric applications

Reduced complexity face recognition using advanced correlation filters and fourier subspace methods for biometric applications
Characterization of palmprints by wavelet signatures via directional context modeling

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On hierarchical palmprint coding with multiple features for personal identification in large databases

IEEE Transactions on Circuits and Systems for Video Technology

A Comparative Study of Palmprint Recognition Algorithms

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present results on classification of palmprint patterns from a large number of classes for biometric verification. We train optimal trade-off correlation filter classifiers with patterns of subregions of the palm as the actual biometric for the person's identity. Our results show that with less than 5 cm2 (less than 1 in2) of the actual palm captured at a low resolution, correlation filter algorithms can verify the authenticity of the palmprint pattern with error rates below 0.5% from as many as 400 different patterns. There is no previous work on biometric palmprint recognition that studies pattern verification of such small palmprint regions with such large number of classes.