Palmprint recognition algorithm with horizontally expanded blanket dimension

Authors:
Xiumei Guo;Weidong Zhou;Yu Wang
Affiliations:
-;-;-
Venue:
Neurocomputing
Year:
2014

Citing 8
Cited 0

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
Fisherpalms based palmprint recognition

Pattern Recognition Letters
Competitive Coding Scheme for Palmprint Verification

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Palmprint identification using feature-level fusion

Pattern Recognition
Palmprint verification based on robust line orientation code

Pattern Recognition
Multiple Resolution Texture Analysis and Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

As an emerging biometric technology, palmprint recognition has been extensively researched due to its easy collection, user friendliness, high verification accuracy and reliability. Blanket dimension is a commonly used fractal dimension and has the multi-resolution characteristics with which the image texture information can be better extracted. In this work, palmprint recognition with blanket dimension and its expansions was investigated. The efficiencies of horizontally and vertically expanded blanket dimensions for extracting the directional feature of palmprint were compared, and a palmprint recognition algorithm based on horizontally expanded blanket dimension (HEBD) was proposed according to the comparison results. Furthermore, a multi-scale HEBD (MHEBD) algorithm for palmprint recognition was also presented, and the MHEBD was demonstrated to be more effective than the single-scale HEBD for feature extraction. The algorithm was evaluated on Hong Kong Polytechnic University (PolyU) database (v2) and CASIA database. The experimental results indicate that the multi-scale HEBD can extract the palmprint features effectively and efficiently with a high recognition rate and less processing time.