15-02-2013, 04:10 PM
An Enhanced Palm Vein Recognition System Using Multi-level Fusion of Multimodal Features and Adaptive Resonance Theory
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ABSTRACT
An improved palm vein recognition system using multimodal
features and neural network classifier has been developed and
presented in this paper. The effects of fusion of multiple
features at various levels have been demonstrated. The shape
and texture features have been considered for recognition of
authenticated users and it is validated using neural network
classifier. The recognition accuracy of the proposed system
has been compared with the existing techniques. It is found
that the recognition accuracy is 99.61% when the multimodal
features fused at matching score level. This proposed
multimodal palm vein recognition system is expected to
provide reliable security.
INTRODUCTION
In the past decade there has been a research concerning
Unimodal analysis of palm vein identification. Unimodal
palm vein identification can be concerned with a variety of
problems such as noisy data, intra-class variations, restricted
degrees of freedom, non-universality, spoof attacks and
unacceptable error rates [1-2,5].
The “Laplacian” multimodal presented by Jain-Gang Wang et
al [3] and The “Laplacian” and “Junction Point” multimodal
presented by Jain-Gang Wang et al [4] have been based on
fusion at imaging level. One of the major issues in imaging
fusion is image alignment or registration, which refer to pixel
– by – pixel alignment of the images. The proposed palm vein
recognition system is expected to overcome some of the
limitations of the existing work.
PALM VEIN IMAGING AND
PREPROCESSING
The photographs of the vein image are in poor contrast
due to glare, and contains irregular shading caused by
various thicknesses of skin and bones. Vein pattern
authentication requires a normalized and enhanced
vein image to authenticate a reliable user. This paper
presents a de-noising and enhancement technique
based on GSZ – shock filter, which focuses on both
noise elimination and edge enhancement.
MULTIPLE FEATURE EXTRACTION
Multiple Feature extraction technique extracts hand
shape features, which is convolved with texture feature to get
the multiple feature set, this process is called as fusion at
feature extraction level. This phase also extracts skeleton,
bifurcation and ending points of the palm vein image [5-6].
Fusion at extraction level
Shape of the palm is extracted by the palm size |A1A2| and
the distance between O1 and O2. These values are constant
according to the hand size, determined by the hand width
computed by the Euclidean distance between the points X
[L1] and X [L2]. Where L1 and L2 are fixed indexes obtained
by trial and error basis.
FEATURE OPTIMIZATION
The number of features used for recognition is more,
and then the network becomes unbelievably complex,
which may increase the size of the network, the
training time, the set size and the classification time. It
is therefore necessary to reduce the number of features
for maintaining the acceptable accuracy.
Adaptive sequential floating forward search (ASFFS)
is one of the best feature selection methods. Most
significant feature can be added with current subset
and the least significant feature can be conditionally
removed from the current subset iteratively by this
method. ASFFS starts from the empty set. After each
forward adding step, it performs backward conditional
removing steps as long as the objective function (Az)
of the current subset increases. In this paper every
feature set such as fused set of hand shape and texture,
skeleton, bifurcation and ending points can be
optimized by the ASFFS optimization technique [12].
PALM VEIN PATTERN MATCHING
The kNN classifier applied for recognition can compare the
unknown palm vein pattern that can be the training pattern
with the database, which consist of the palm vein pattern of
registered users. Hence this approach is lazy learning or
memory based learning approach, which can be lead to
increase the computational complexity and cost during the
testing process. The proposed palm vein system can be done
the pattern matching using ART1, which can learn and
recognize the binary palm vein pattern. This ART1 matching
module can be constructed by an attentional subsystem and an
orienting subsystem. The attentional subsystem is responsible
for competitive learning and enhancing the palm vein pattern
by suppressing noise. The orienting subsystem can work as a
novelty detector.
CONCLUSION
The palm vein recognition system using multilevel
fusion of multimodal features and neural network
classifier has been developed. The shape and texture
features have been extracted and multimodal features
have been obtained at feature extraction level as well
as matching score level. The Neural network classifier
has been used to classify the vein patterns for making
necessary decision. It is concluded from the analysis
that the multimodal palm vein recognition system
provides better performance compared unimodal
features.