Abstract: With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.
Abstract: Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.
Abstract: Storm Event Analysis (SEA) provides a method to define rainfalls events as storms where each storm has its own amount and duration. By modelling daily probability of different types of storms, the onset, offset and cycle of rainfall seasons can be determined and investigated. Furthermore, researchers from the field of meteorology will be able to study the dynamical characteristics of rainfalls and make predictions for future reference. In this study, four categories of storms; short, intermediate, long and very long storms; are introduced based on the length of storm duration. Daily probability models of storms are built for these four categories of storms in Peninsular Malaysia. The models are constructed by using Bernoulli distribution and by applying linear regression on the first Fourier harmonic equation. From the models obtained, it is found that daily probability of storms at the Eastern part of Peninsular Malaysia shows a unimodal pattern with high probability of rain beginning at the end of the year and lasting until early the next year. This is very likely due to the Northeast monsoon season which occurs from November to March every year. Meanwhile, short and intermediate storms at other regions of Peninsular Malaysia experience a bimodal cycle due to the two inter-monsoon seasons. Overall, these models indicate that Peninsular Malaysia can be divided into four distinct regions based on the daily pattern for the probability of various storm events.
Abstract: Multimodal biometric systems integrate the data presented by multiple biometric sources, hence offering a better performance than the systems based on a single biometric modality. Although the coupling of biometric systems can be done at different levels, the fusion at the scores level is the most common since it has been proven effective than the rest of the fusion levels. However, the scores from different modalities are generally heterogeneous. A step of normalizing the scores is needed to transform these scores into a common domain before combining them. In this paper, we study the performance of several normalization techniques with various fusion methods in a context relating to the merger of three unimodal systems based on the face, the palmprint and the fingerprint. We also propose a new adaptive normalization method that takes into account the distribution of client scores and impostor scores. Experiments conducted on a database of 100 people show that the performances of a multimodal system depend on the choice of the normalization method and the fusion technique. The proposed normalization method has given the best results.
Abstract: Prior research evidenced that unimodal biometric
systems have several tradeoffs like noisy data, intra-class variations,
restricted degrees of freedom, non-universality, spoof attacks, and
unacceptable error rates. In order for the biometric system to be more
secure and to provide high performance accuracy, more than one
form of biometrics are required. Hence, the need arise for multimodal
biometrics using combinations of different biometric modalities. This
paper introduces a multimodal biometric system (MMBS) based on
fusion of whole dorsal hand geometry and fingerprints that acquires
right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG)
shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100
volunteers were acquired using the designed prototype. The acquired
images were found to have good quality for all features and patterns
extraction to all modalities. HG features based on the hand shape
anatomical landmarks were extracted. Robust and fast algorithms for
FP minutia points feature extraction and matching were used. Feature
vectors that belong to similar biometric traits were fused using
feature fusion methodologies. Scores obtained from different
biometric trait matchers were fused using the Min-Max
transformation-based score fusion technique. Final normalized scores
were merged using the sum of scores method to obtain a single
decision about the personal identity based on multiple independent
sources. High individuality of the fused traits and user acceptability
of the designed system along with its experimental high performance
biometric measures showed that this MMBS can be considered for
med-high security levels biometric identification purposes.
Abstract: Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.
Abstract: We report a computational study of the spreading
dynamics of a viral infection in a complex (scale-free) network. The
final epidemic size distribution (FESD) was found to be unimodal or
bimodal depending on the value of the basic reproductive
number R0 . The FESDs occurred on time-scales long enough for
intermediate-time epidemic size distributions (IESDs) to be important
for control measures. The usefulness of R0 for deciding on the
timeliness and intensity of control measures was found to be limited
by the multimodal nature of the IESDs and by its inability to inform
on the speed at which the infection spreads through the population. A
reduction of the transmission probability at the hubs of the scale-free
network decreased the occurrence of the larger-sized epidemic events
of the multimodal distributions. For effective epidemic control, an
early reduction in transmission at the index cell and its neighbors was
essential.
Abstract: In this paper, a pipelined version of genetic algorithm,
called PLGA, and a corresponding hardware platform are described.
The basic operations of conventional GA (CGA) are made pipelined
using an appropriate selection scheme. The selection operator, used
here, is stochastic in nature and is called SA-selection. This helps
maintaining the basic generational nature of the proposed pipelined
GA (PLGA). A number of benchmark problems are used to compare
the performances of conventional roulette-wheel selection and the
SA-selection. These include unimodal and multimodal functions with
dimensionality varying from very small to very large. It is seen that
the SA-selection scheme is giving comparable performances with
respect to the classical roulette-wheel selection scheme, for all the
instances, when quality of solutions and rate of convergence are considered.
The speedups obtained by PLGA for different benchmarks
are found to be significant. It is shown that a complete hardware
pipeline can be developed using the proposed scheme, if parallel
evaluation of the fitness expression is possible. In this connection
a low-cost but very fast hardware evaluation unit is described.
Results of simulation experiments show that in a pipelined hardware
environment, PLGA will be much faster than CGA. In terms of
efficiency, PLGA is found to outperform parallel GA (PGA) also.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.
Abstract: Single biometric modality recognition is not able to meet the high performance supplies in most cases with its application become more and more broadly. Multimodal biometrics identification represents an emerging trend recently. This paper investigates a novel algorithm based on fusion of both fingerprint and fingervein biometrics. For both biometric recognition, we employ the Monogenic Local Binary Pattern (MonoLBP). This operator integrate the orginal LBP (Local Binary Pattern ) with both other rotation invariant measures: local phase and local surface type. Experimental results confirm that a weighted sum based proposed fusion achieves excellent identification performances opposite unimodal biometric systems. The AUC of proposed approach based on combining the two modalities has very close to unity (0.93).