Abstract:
Sclera vein recognition is shown to be a promising
method for human identification. However, its matching speed
is slow, which could impact its application for real-time applications.
To improve the matching efficiency, we proposed a new
parallel sclera vein recognition method using a two-stage parallel
approach for registration and matching. First, we designed a
rotation- and scale-invariant Y shape descriptor based feature
extraction method to efficiently eliminate most unlikely matches.
Second, we developed a weighted polar line sclera descriptor
structure to incorporate mask information to reduce GPU memory
cost. Third, we designed a coarse-to-fine two-stage matching
method. Finally, we developed a mapping scheme to map the
subtasks to GPU processing units. The experimental results show
that our proposed method can achieve dramatic processing speed
improvement without compromising the recognition accuracy.
INTRODUCTION
THE sclera is the opaque and white outer layer of the eye.
The blood vessel structure of sclera is formed randomly
and is unique to each person , which can be used for
human’s identification . Several researchers have designed
different Sclera vein recognition methods and have shown
that it is promising to use Sclera vein recognition for human
identification.
In [4], Crihalmeanu and Ross proposed three
approaches: Speed Up Robust Features (SURF)-based method,
minutiae detection, and direct correlation matching for feature
registration and matching. Within these three methods,
the SURF method achieves the best accuracy. It takes an
average of 1.5 seconds1 using the SURF method to per form a one-to-one matching. In [3], Zhou et. al. proposed
line descriptor-based method for sclera vein recognition.
The matching step (including registration) is the most timeconsuming
step in this sclera vein recognition system, which
costs about 1.2 seconds to perform a one-to-one matching.
Both speed was calculated using a PC with Intel® Core™
2 Duo 2.4GHz processors and 4 GB DRAM. Currently,
Sclera vein recognition algorithms [3, 4] are designed using
central processing unit (CPU)-based systems.
As discussed
in [7], CPU-based systems are designed as sequential processing
devices, which may not be efficient in data processing
where the data can be parallelized. Because of large time
consumption in the matching step, Sclera vein recognition
using sequential-based method would be very challenging to
be implemented in a real time biometric system, especially
when there is large number of templates in the database for
matching.
GPUs (as abbreviation of General purpose Graphics
Processing Units: GPGPUs) are now popularly used for
parallel computing to improve the computational processing
speed and efficiency [8-20].
The highly parallel structure
of GPUs makes them more effective than CPUs for
data processing where processing can be performed in
parallel. GPUs have been widely used in biometrics
recognition such as: speech recognition [8], text detection [9],
handwriting recognition [10], and face recognition [14].
In iris recognition [15], GPU was used to extract the features,
construct descriptors, and match templates. GPUs are also used
for object retrieval and image search [16-19]. Park et al. [20]
designed the performance evaluation of image processing
algorithms, such as linear feature extraction and multi-view
stereo matching, on GPUs. However, these approaches were
designed for their specific biometric recognition applications
and feature searching methods.
Therefore they may not be
efficient for Sclera vein recognition.
Compute Unified Device Architecture (CUDA), the computing
engine of NVIDIA GPUs, is used in this research.
CUDA is a highly parallel, multithreaded, many-core processor
with tremendous computational power [21].
It supports not
only a traditional graphics pipeline but also computation on
non-graphical data. More importantly, it offers an easier programming
platform which outperforms its CPU counterparts in
terms of peak arithmetic intensity and memory bandwidth [22].
In this research, the goal is not to develop a unified
strategy to parallelize all sclera matching methods because
each method is quite different from one another and would
need customized design. To develop an efficient parallel computing
scheme, it would need different strategies for different
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