10-04-2017, 09:22 PM
ABSTRACT
Developing accurate and robust palmprint verification
algorithms is one of the key issues in automatic palmprint
recognition systems. Recently, orientation based coding
algorithms, such as Competitive Code (CompCode) and
Orthogonal Line Ordinal Features (OLOF), have been
proposed and have been attracting much research attention.
Such algorithms could achieve high accuracy with high
feature matching speed for real time implementation. By
investigating the relationship between these two different
coding schemes, we propose in this paper a feature-level
fusion scheme for palmprint verification. Only the stable
features which are consistent between the two codes are
extracted for matching. The experimental results on the
public palmprint database show that the proposed fusion
code could achieve at least 14% EER (Equal Error Rate)
reduction compared with either of the original codes.
Index Terms palmprint verification, feature-level
fusion, orientation coding
1. INTRODUCTION
As a substitution or complementary technology for
traditional personal authentication methods, biometric
techniques are becoming more and more popular in public
security applications. Biometrics is the study of methods for
uniquely recognizing humans based upon one or more
intrinsic physical or behavioral traits, including the
extensively studied fingerprint, face, iris, speech, hand
geometry, etc [1]. Palmprint authentication as an emerging
biometrics technology has been drawing much attention in
both academic and industry societies [2-10] because it owns
many merits, such as robustness, user-friendliness and costeffectiveness.
Palmprint is composed of three main kinds of features:
principal lines (usually three dominant lines on the palm),
wrinkles (weaker and more irregular lines) and minutia
(ridge and valley features which are similar to those in
fingerprint images) [2]. Unlike fingerprint which requires a
high resolution (about 500dpi) to capture clear minutia
features, the principal lines and wrinkles in a palmprint
could be captured under a low resolution (<100dpi) and
they could provide enough discriminatory information for
personal identification. Fig. 1-a shows the Region of
Interest (ROI) of a palmprint sample image [2].
Competitive Code (CompCode) [8] and Orthogonal Line
Ordinal Features (OLOF) [9] are state-of-the-art algorithms
for palmprint verification because they could achieve very
high accuracy with a high matching speed. Taking palm
lines as negative lines, Kong and Zhang [8] proposed to
extract and code the orientation of palm lines for palmprint
verification. They applied six Gabor filters to the image and
selected one main orientation for each local region. Sun et
al. [9] proposed a new palmprint representation. They
compared two elongated line-like image regions, which are
orthogonal in orientation, and then assigned one bit feature
code. Three bits are then obtained by using three different
orientated ordinal filters. Two example maps of CompCode
and OLOF extracted from Fig. 1-a are shown in Fig. 1-b
and Fig. 1-c.
(a) (b) ©
Fig.1 (a) The ROI of a sample palmprint image and the
extracted (b) CompCode map and © OLOF map. Different
features are represented by using different gray values.
From Fig. 1, we can see that the OLOF and CompCode
maps of the same palmprint are different but both of them
have clear representation of principal lines and wrinkles.
Since principal lines and wrinkles are the most stable and
robust features in low resolution palmprint images, it
inspires us to develop a feature level fusion scheme to
further enhance those stable features for a more robust
feature extraction. After investigating the correlation
between OLOF and CompCode, in this paper, we propose
such a feature level fusion scheme according to the
consistency between the two different codes.
The rest of the paper is organized as follows. Section 2
briefly reviews CompCode and OLOF. Section 3 analyzes
the correlation between them and then fuses them. Section 4
verifies the proposed scheme on a large public palmprint
database. Finally, Section 5 concludes the paper.
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