A Novel Hand-Based Personal Identification Approach

Authors:
Miao Qi;Yinghua Lu;Hongzhi Li;Rujuan Wang;Jun Kong
Affiliations:
Computer School, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

Citing 7
Cited 0

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
RETRACTED: Invariance image analysis using modified Zernike moments

Pattern Recognition Letters
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture-based palmprint retrieval using a layered search scheme for personal identification

IEEE Transactions on Multimedia
Characterization of palmprints by wavelet signatures via directional context modeling

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hand-based personal identification is a stable and reliable biometrically technique in the field of personal identity recognition. In this paper, both hand shape and palmprint texture features are extracted to facilitate a coarse-to-fine dynamic identification task. The wavelet zero-crossing method is first used to extract hand shape features to guide the fast selection of a small set of similar candidates from the database. Then, a circular Gabor filter, which is robust against brightness, and modified Zernike moments methods are used to extract the features of palmprint. And one-class-one-network (Back-Propagation Neural Network (BPNN) classification structure is employed for final classification. The experimental results show the effectiveness and accuracy of the proposed approach.