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COMBINING LEFT AND RIGHT PALMPRINT IMAGES FOR MORE ACCURATE
PERSONAL IDENTIFICATION
ABSTRACT
Multibiometrics can provide higher identificationaccuracy than single biometrics, so it is
more suitable forsome real-world personal identification applications that needhigh-standard
security. Among various biometrics technologies,palmprint identification has received much
attention because ofits good performance. Combining the left and right palmprintimages to
perform multibiometrics is easy to implement and canobtain better results. However, previous
studies did not explorethis issue in depth. In this paper, we proposed a novel frameworkto
perform multibiometrics by comprehensively combining theleft and right palmprint images. This
framework integrated threekinds of scores generated from the left and right palmprintimages to
perform matching score-level fusion. The first twokinds of scores were, respectively, generated
from the left andright palmprint images and can be obtained by any palmprintidentification
method, whereas the third kind of score wasobtained using a specialized algorithm proposed in
this paper.As the proposed algorithm carefully takes the nature of the leftand right palmprint
images into account, it can properly exploitthe similarity of the left and right palmprints of the
same subject.Moreover, the proposed weighted fusion scheme allowed perfectidentification
performance to be obtained in comparison withprevious palmprint identification methods.

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Combining left and right palmprint images for more accurate personal identification

  • 1. COMBINING LEFT AND RIGHT PALMPRINT IMAGES FOR MORE ACCURATE PERSONAL IDENTIFICATION ABSTRACT Multibiometrics can provide higher identificationaccuracy than single biometrics, so it is more suitable forsome real-world personal identification applications that needhigh-standard security. Among various biometrics technologies,palmprint identification has received much attention because ofits good performance. Combining the left and right palmprintimages to perform multibiometrics is easy to implement and canobtain better results. However, previous studies did not explorethis issue in depth. In this paper, we proposed a novel frameworkto perform multibiometrics by comprehensively combining theleft and right palmprint images. This framework integrated threekinds of scores generated from the left and right palmprintimages to perform matching score-level fusion. The first twokinds of scores were, respectively, generated from the left andright palmprint images and can be obtained by any palmprintidentification method, whereas the third kind of score wasobtained using a specialized algorithm proposed in this paper.As the proposed algorithm carefully takes the nature of the leftand right palmprint images into account, it can properly exploitthe similarity of the left and right palmprints of the same subject.Moreover, the proposed weighted fusion scheme allowed perfectidentification performance to be obtained in comparison withprevious palmprint identification methods.