1,774 research outputs found
Biometric Authentication using Nonparametric Methods
The physiological and behavioral trait is employed to develop biometric
authentication systems. The proposed work deals with the authentication of iris
and signature based on minimum variance criteria. The iris patterns are
preprocessed based on area of the connected components. The segmented image
used for authentication consists of the region with large variations in the
gray level values. The image region is split into quadtree components. The
components with minimum variance are determined from the training samples. Hu
moments are applied on the components. The summation of moment values
corresponding to minimum variance components are provided as input vector to
k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU
database consisting of 45 subjects. The number of subjects with zero False
Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance
Rate [FAR] was 45. This paper addresses the computational load reduction in
off-line signature verification based on minimal features using k-means, fuzzy
k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and
FAR of 10% was achieved using k-nn classifier. The signature is a biometric,
where variations in a genuine case, is a natural expectation. In the genuine
signature, certain parts of signature vary from one instance to another. The
system aims to provide simple, fast and robust system using less number of
features when compared to state of art works.Comment: 20 page
A comparative study of general fuzzy min-max neural networks for pattern classification problems
© 2019 Elsevier B.V. General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance influencing factors, advantages, and drawbacks of the general fuzzy min-max neural network on pattern classification problems. The subjects of this study include (1) the impact of maximum hyperbox size, (2) the influence of the similarity threshold and measures on the agglomerative learning algorithm, (3) the effect of data presentation order, (4) comparative performance evaluation of the GFMM with other types of fuzzy min-max neural networks and prevalent machine learning algorithms. The experimental results on benchmark datasets widely used in machine learning showed overall strong and weak points of the GFMM classifier. These outcomes also informed potential research directions for this class of machine learning algorithms in the future
Adaptive noise reduction and code matching for IRIS pattern recognition system
Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system
Anonymous subject identification and privacy information management in video surveillance
The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
State of the Art in Biometric Key Binding and Key Generation Schemes
Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems
The doctoral research abstract. Vol:9 2016 / Institute of Graduate Studies, UiTM
FOREWORD:
Seventy three doctoral graduands will be receiving their scroll today signifying their
achievements in completing their PhD journey. The novelty of their research is shared with
you through The Doctoral Abstracts on this auspicious occasion, UiTM 84th Convocation.
We are indeed proud that another 73 scholarly contributions to the world of knowledge
and innovation have taken place through their doctoral research ranging from Science and
Technology, Business and Administration, and Social Science and Humanities.
As we rejoice and celebrate your achievement, we would like to acknowledge
dearly departed Dr Halimi Zakaria’s scholarly contribution entitled
“Impact of Antecedent Factors on Collaborative Technologies Usage
among Academic Researchers in Malaysian Research Universities”. He
has left behind his discovery to be used by other researchers in their quest
of pursuing research in the same area, a discovery that his family can be
proud of.
Graduands, earning your PhD is not the end of discovering new ideas,
invention or innovation but rather the start of discovering something
new. Enjoy every moment of its discovery and embrace that life is
full of mystery and treasure that is waiting for you to unfold. As
you unfold life’s mystery, remember you have a friend to count
on, and that friend is UiTM.
Congratulations for completing this academic journey. Keep
UiTM close to your heart and be our ambassador wherever
you go. / Prof Emeritus Dato’ Dr Hassan Said
Vice Chancellor
Universiti Teknologi MAR
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