07-10-2014, 03:41 PM
A Survey on Security in Palmprint Recognition:
A Biometric Trait
A Survey.pdf (Size: 265.88 KB / Downloads: 34)
Abstract
— Biometric based authentication and recognition,
the science of using physical or behavioral characteristic for
identity verification is becoming a security principal in many
areas. Their utilization as an authentication and recognition
technology has become widespread from door access to
electronic commerce. Security is a very important aspect in the
biometric system itself. Biometric recognition system includes
one of the biometric trait as palmprint, Palmprint recognition
has been investigated over last fifteen years. During this period,
many different problems related to palmprint recognition and
securities in the system have been addressed. This paper
provides an overview of palmprint research, describing in
capture devices, preprocessing, verification, palmprint -related
fusion and measures of security and for protecting users’
privacy and palmprint system. Finally some conclusion and
suggestion is offered.
INTRODUCTION
The inner surface of the palm normally contains three
flexion creases, secondary creases and ridges. The flexion
creases are also called principal lines and secondary creases
are called wrinkles.
Palmprint research employs either high resolution or low
resolution images. High resolution images are suitable for
forensic applications such as criminal detection. Low
resolution images are more suitable for commercial and civil
application such as access control. Most of the research using
palm print verification uses low resolution images [2]. In
general the high resolution can be 400 dpi or more and low
resolution can be 150 dpi or less. Figure 1 shows a part of a
high resolution palmprint image and a low resolution
palmprint image. One can extract ridges, singular points and
minutia points as features from high resolution images while
in low resolution images principal lines, wrinkles and
textures can be extracted.
Palmprint Scanners
Researchers utilize four types of sensors: CCD-based
palmprint scanners, digital cameras, digital scanners and
video cameras to collect palmprint images. Figure 4 shows a
CCD-based palmprint scanner developed by The Hong Kong
Polytechnic University[1]. CCD-based palmprint scanners
capture high quality palmprint images and align palms
accurately because the scanners have pegs for guiding the
placement of hands. These scanners simplify the
development of recognition algorithms because the images
are captured in a controlled environment.
DISCUSSION AND CONCLUSION
Before the end of this paper, we would like to re-mention
some papers that are very worthy to read carefully. Our first
suggestion is Han's work [6], which is a very complete work.
We especially appreciate his palmprint scanner described in
this work that can collect images of whole hands and use pegs
for hand placement. For verification, we recommend relation
filter approach. For real-time large database identification,
PalmCode, Fusion Code and Competitive Code and the
theory of coding methods will be more suitable. Biometric
fusion is in fact an application of information fusion and
combined classifiers. Many excellent papers have been
published in these two fields security, we also do not
emphasize on any paper because the literature of palmprint
security is very small. In face recognition literature, many
researchers design algorithms based on prior knowledge of
the face. To optimize the recognition performance in terms of
speed and accuracy, we expect that more algorithms are
designed based on the prior knowledge of palmprints.
Different template formats may require different measures
for tem- plate protection. More research should be put into
security and privacy issues. For biometric fusion, the authors
recommend combining Iris Code—the commercial iris
recognition algorithm and Competitive Code or other coding
methods for high-speed large-scale personal identification
because these algorithms share a number of important
properties (e.g. high speed matching). Even though Iris Code
does not accumulate false acceptance rates when the number
templates in database increases, its false reject rate still
increases. Some issues in using palmprints for personal
identification have not been well addressed. For instance, we
know that ridges in palmprints are stable for a person's whole
life but the stability of principal lines and wrinkles has not
been systemically investigated