This document presents a multimodal biometric verification system that uses multiple fingerprint matchers to improve accuracy. It combines two fingerprint matching techniques - Spatial Grey Level Dependence Method (SGLDM) and Filterbank-based matching. Matching scores from the two techniques are normalized and combined using sum rule fusion. The system was tested on a fingerprint database and experimental results showed the proposed fusion strategy reduces total error rate, improving overall system accuracy compared to single matcher systems.
Integrating Fusion levels for Biometric Authentication SystemIOSRJECE
— Recently a lot of works are presented in the literature for the multimodal biometric authentication. And such biometric systems have been widely accepted with increasing accuracy rates and population coverage, reducing the vulnerability to spoofing. This paper descripts about the proposed multimodal biometric system that combines the feature extraction level and the score level fusion of iris and face unimodal biometric systems in order to take advantage of both fusion techniques. The experimental results shows the performance of the multimodal and multilevel fusion techniques which are analysed using TRR and TAR to study the recognition behaviour of the approach system. From the ROC Curve plotted, the performance of the proposed system is better compared to the individual fusion techniques.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and methodologies for the extraction of features are also discussed.
Feature Level Fusion of Multibiometric Cryptosystem in Distributed SystemIJMER
ABSTRACT: Multibiometrics is the combination of one or more biometrics (e.g., Fingerprint, Iris, and Face). Researchers
are focusing on how to provide security to the system, the template which was generated from the biometric need to be
protected. The problems of unimodal biometrics are solved by multibiometrics. The main objective is to provide a security to
the biometric template by generating a secure sketch by making use of multibiometric cryptosystem and which is stored in a
database. Once the biometric template is stolen it becomes a serious issue for the security of the system and also for user
privacy. In the existing approach, feature level fusion is used to combine the features securely with well-known biometric
cryptosystems namely fuzzy vault and fuzzy commitment. The drawbacks of existing system include accuracy of the biometric
need to be improved and the noises in the biometrics also need to be reduced. The proposed work is to enhance the security
using multibiometric cryptosystem in distributed system applications like e-commerce transactions, e-banking and ATM.
Keywords: Biometric Cryptosystem, Error correcting code, Fingerprint, Iris, Multibiometrics, Unimodal biometrics.
Integrating Fusion levels for Biometric Authentication SystemIOSRJECE
— Recently a lot of works are presented in the literature for the multimodal biometric authentication. And such biometric systems have been widely accepted with increasing accuracy rates and population coverage, reducing the vulnerability to spoofing. This paper descripts about the proposed multimodal biometric system that combines the feature extraction level and the score level fusion of iris and face unimodal biometric systems in order to take advantage of both fusion techniques. The experimental results shows the performance of the multimodal and multilevel fusion techniques which are analysed using TRR and TAR to study the recognition behaviour of the approach system. From the ROC Curve plotted, the performance of the proposed system is better compared to the individual fusion techniques.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and methodologies for the extraction of features are also discussed.
Feature Level Fusion of Multibiometric Cryptosystem in Distributed SystemIJMER
ABSTRACT: Multibiometrics is the combination of one or more biometrics (e.g., Fingerprint, Iris, and Face). Researchers
are focusing on how to provide security to the system, the template which was generated from the biometric need to be
protected. The problems of unimodal biometrics are solved by multibiometrics. The main objective is to provide a security to
the biometric template by generating a secure sketch by making use of multibiometric cryptosystem and which is stored in a
database. Once the biometric template is stolen it becomes a serious issue for the security of the system and also for user
privacy. In the existing approach, feature level fusion is used to combine the features securely with well-known biometric
cryptosystems namely fuzzy vault and fuzzy commitment. The drawbacks of existing system include accuracy of the biometric
need to be improved and the noises in the biometrics also need to be reduced. The proposed work is to enhance the security
using multibiometric cryptosystem in distributed system applications like e-commerce transactions, e-banking and ATM.
Keywords: Biometric Cryptosystem, Error correcting code, Fingerprint, Iris, Multibiometrics, Unimodal biometrics.
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
The biometric technology is used to identify
individuals effectively compared to existing traditional
methods. In this paper we propose Bimodal Biometric System
using Multiple Transformation features of Fingerprint and
Iris (BBMFI). The iris image is preprocessed to generate iris
template. The two level Discrete Wavelet Transformation
(DWT) is applied on iris template and Discrete Cosine
Transformation (DCT) is performed on second level low
frequency band to generate DCT coefficients which results in
features of iris. The fingerprint is preprocessed to obtain
Region of Interest (ROI) and segmented into four cells. Then
the DWT is applied on each cell to derive approximation band
and detailed bands. The Fast Fourier Transformation (FFT)
is applied on approximation band to compute absolute values
that results in features of fingerprint. The iris features and
fingerprint features are fused by concatenation to obtain final
set of features. The final feature vector of test and database
are compared using Euclidean distance matching. It is observed
that the values of Total Success Rate (TSR), False Rejection
Rate (FRR) and False Acceptance Rate (FAR) are improved in
the proposed system compared to existing algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Personal identification using multibiometrics score level fusioneSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Biometric Template Protection With Robust Semi – Blind Watermarking Using Ima...CSCJournals
This paper addresses a biometric watermarking technology sturdy towards image manipulations, like JPEG compression, image filtering, and additive noise. Application scenarios include information transmission between client and server, maintaining e-database and management of signatures through insecure distribution channels. Steps involved in this work are, a) generation of binary signature code for biometric, b) embedding of the binary signature to the host image using intrinsic local property, that ensures signature protection, c) host image is then made exposed to various attacks and d) signature is extracted and matched based on an empirical threshold to verify the robustness of proposed embedding method. Embedding relies on binary signature manipulating the lower order AC coefficients of Discrete Cosine Transformed sub-blocks of host image. In the prediction phase, DC values of the nearest neighbor DCT blocks is utilized to predict the AC coefficients of centre block. Surrounding DC values of a DCT blocks are adaptively weighed for AC coefficients prediction. Linear programming is used to calculate the weights with respect to the image content. Multiple times embedding of watermark ensures robustness against common signal processing operations (filtering, enhancement, rescaling etc.) and various attacks. The proposed algorithm is tested for 50 different types of host images and public data collection, DB3, FVC2002. FAR and FRR are compared with other methods to show the improvement.
The Survey of Architecture of Multi-Modal (Fingerprint and Iris Recognition) ...IJERA Editor
Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person’s identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
Measuring memetic algorithm performance on image fingerprints datasetTELKOMNIKA JOURNAL
Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
For securing personal identifications and highly secure identification problems, biometric technologies will
provide higher security with improved accuracy. This has become an emerging technology in recent years due to
the transaction frauds, security breaches and personal identification etc. The beauty of biometric technology is it
provides a unique code for each person and it can’t be copied or forged by others. To overcome the draw backs
of finger print identification systems, here in this paper we proposed a palm print based personal identification
system, which is a most promising and emerging research area in biometric identification systems due to its
uniqueness, scalability, faster execution speed and large area for extracting the features. It provides higher
security over finger print biometric systems with its rich features like wrinkles, continuous ridges, principal
lines, minutiae points, and singular points. The main aim of proposed palm print identification system is to
implement a system with higher accuracy and increased speed in identifying the palm prints of several users.
Here, in this we presented a highly secured palm print identification system with extraction of region of interest
(ROI) with morphological operation there by applying un-decimated bi-orthogonal wavelet (UDBW) transform
to extract the low level features of registered palm prints to calculate its feature vectors (FV) then after the
comparison is done by measuring the distance between registered palm feature vector and testing palm print
feature vector. Simulation results show that the proposed biometric identification system provides more
accuracy and reliable recognition rate
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Role of fuzzy in multimodal biometrics systemKishor Singh
Person identification is possible through the biometrics using their physiological and behavioral characteristics such
as face, ear, thumb print, voice, signature and key stock. Unimodal biometric systems face a range of problems, including noisy
data, intra-class versions, small liberty, non-university, spoof assaults, and unsustainable error rates. Some of these drawbacks
can be overcome by multimodal biometric technologies, which incorporate data from various information sources. In this paper
we work on multimodal biometric using three modalities face, ear and foot to find the optimal results using fuzzy fusion
mechanism and produces final identification decision via a fuzzy rules that enhance the quality of multimodalities biometric
system.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
The biometric technology is used to identify
individuals effectively compared to existing traditional
methods. In this paper we propose Bimodal Biometric System
using Multiple Transformation features of Fingerprint and
Iris (BBMFI). The iris image is preprocessed to generate iris
template. The two level Discrete Wavelet Transformation
(DWT) is applied on iris template and Discrete Cosine
Transformation (DCT) is performed on second level low
frequency band to generate DCT coefficients which results in
features of iris. The fingerprint is preprocessed to obtain
Region of Interest (ROI) and segmented into four cells. Then
the DWT is applied on each cell to derive approximation band
and detailed bands. The Fast Fourier Transformation (FFT)
is applied on approximation band to compute absolute values
that results in features of fingerprint. The iris features and
fingerprint features are fused by concatenation to obtain final
set of features. The final feature vector of test and database
are compared using Euclidean distance matching. It is observed
that the values of Total Success Rate (TSR), False Rejection
Rate (FRR) and False Acceptance Rate (FAR) are improved in
the proposed system compared to existing algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Personal identification using multibiometrics score level fusioneSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Biometric Template Protection With Robust Semi – Blind Watermarking Using Ima...CSCJournals
This paper addresses a biometric watermarking technology sturdy towards image manipulations, like JPEG compression, image filtering, and additive noise. Application scenarios include information transmission between client and server, maintaining e-database and management of signatures through insecure distribution channels. Steps involved in this work are, a) generation of binary signature code for biometric, b) embedding of the binary signature to the host image using intrinsic local property, that ensures signature protection, c) host image is then made exposed to various attacks and d) signature is extracted and matched based on an empirical threshold to verify the robustness of proposed embedding method. Embedding relies on binary signature manipulating the lower order AC coefficients of Discrete Cosine Transformed sub-blocks of host image. In the prediction phase, DC values of the nearest neighbor DCT blocks is utilized to predict the AC coefficients of centre block. Surrounding DC values of a DCT blocks are adaptively weighed for AC coefficients prediction. Linear programming is used to calculate the weights with respect to the image content. Multiple times embedding of watermark ensures robustness against common signal processing operations (filtering, enhancement, rescaling etc.) and various attacks. The proposed algorithm is tested for 50 different types of host images and public data collection, DB3, FVC2002. FAR and FRR are compared with other methods to show the improvement.
The Survey of Architecture of Multi-Modal (Fingerprint and Iris Recognition) ...IJERA Editor
Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person’s identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
Measuring memetic algorithm performance on image fingerprints datasetTELKOMNIKA JOURNAL
Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
For securing personal identifications and highly secure identification problems, biometric technologies will
provide higher security with improved accuracy. This has become an emerging technology in recent years due to
the transaction frauds, security breaches and personal identification etc. The beauty of biometric technology is it
provides a unique code for each person and it can’t be copied or forged by others. To overcome the draw backs
of finger print identification systems, here in this paper we proposed a palm print based personal identification
system, which is a most promising and emerging research area in biometric identification systems due to its
uniqueness, scalability, faster execution speed and large area for extracting the features. It provides higher
security over finger print biometric systems with its rich features like wrinkles, continuous ridges, principal
lines, minutiae points, and singular points. The main aim of proposed palm print identification system is to
implement a system with higher accuracy and increased speed in identifying the palm prints of several users.
Here, in this we presented a highly secured palm print identification system with extraction of region of interest
(ROI) with morphological operation there by applying un-decimated bi-orthogonal wavelet (UDBW) transform
to extract the low level features of registered palm prints to calculate its feature vectors (FV) then after the
comparison is done by measuring the distance between registered palm feature vector and testing palm print
feature vector. Simulation results show that the proposed biometric identification system provides more
accuracy and reliable recognition rate
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Role of fuzzy in multimodal biometrics systemKishor Singh
Person identification is possible through the biometrics using their physiological and behavioral characteristics such
as face, ear, thumb print, voice, signature and key stock. Unimodal biometric systems face a range of problems, including noisy
data, intra-class versions, small liberty, non-university, spoof assaults, and unsustainable error rates. Some of these drawbacks
can be overcome by multimodal biometric technologies, which incorporate data from various information sources. In this paper
we work on multimodal biometric using three modalities face, ear and foot to find the optimal results using fuzzy fusion
mechanism and produces final identification decision via a fuzzy rules that enhance the quality of multimodalities biometric
system.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
The increasing use of distributed authentication architecture
has made interoperability of systems an important issue. Interoperabil ity of systems affects the maturity of the technology and also improves confidence of users in the technology. Biometric systems are not immune to the concerns of interoperability. Interoperability of fingerprint sensors and its effect on the overall performance of the recognition system is an area of interest with a considerable amount of work directed
towards it. This research analyzed effects of interoperability on error rates for fingerprint datasets captured from two optical sensors and a capacitive sensor when using a single commercially available fingerprint
matching algorithm. The main aim of this research was to emulate a
centralized storage and matching architecture with multiple acquisition
stations. Fingerprints were collected from 44 individuals on all three sensors and interoperable False Reject Rates of less than .31% were achieved using two different enrolment strategies.
Review of Multimodal Biometrics: Applications, Challenges and Research AreasCSCJournals
Biometric systems for today’s high security applications must meet stringent performance requirements. The fusion of multiple biometrics helps to minimize the system error rates. Fusion methods include processing biometric modalities sequentially until an acceptable match is obtained. More sophisticated methods combine scores from separate classifiers for each modality. This paper is an overview of multimodal biometrics, challenges in the progress of multimodal biometrics, the main research areas and its applications to develop the security system for high security areas
novel method of identifying fingerprint using minutiae matching in biometric ...INFOGAIN PUBLICATION
Fingerprint is one of the best apparatus to identify human because of its uniqueness, details information, hard to change and long-term indicators of human identity where there are several biometric feature that can be recycled to endorse the individuality. Identification of fingerprint is very important in forensic science, trace any part of human, collection of crime part and proof from a crime. This paper presents a new method of identifying fingerprint in biometrics security system. Fingerprint is one of the best example in biometric security because it can identify personal information and it is much secure than any other biometric identification system. The experimental result exhibits the performance of the proposed method.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking
IRJET-Gaussian Filter based Biometric System Security EnhancementIRJET Journal
M.Selvi, T.Manickam, C.N.Marimuthu"Gaussian Filter based Biometric System Security Enhancement", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
A novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. To enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment.
The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. Multi-biometric and Multi-attack protection method which targets to overcome part of these limitations through the use of Image Quality Assessment (IQA).
Moreover, being software-based, it presents the usual advantages of this type of approaches: fast, as it only needs one image (i.e., the same sample acquired for biometric recognition) to detect whether it is real or fake, non-intrusive; user-friendly (transparent to the user), cheap and easy to embed in already functional systems and no hardware is required).
Overlapped Fingerprint Separation for Fingerprint AuthenticationIJERA Editor
Overlapped fingerprints captured at the crime scene plays significant role as an evidence to capture the criminals. As latent fingerprints are the accidently left skin impressions, so these are found to be with broken ridge composition, overlapped patterns and spoiled minutiae information. The Graphical User Interface (GUI) system is developed by using MATLAB R2015a software. This project also includes the development of standalone program for this system. The main purpose of GUI development is to get the value of real end points and real-branch points of a overlapped fingerprint image. The value of this point is used in fingerprint image matching process to identify the owner of an overlapped fingerprint image. The image enhancement consists of several process such as histogram equalization process, enhancement by Fast Fourier Transform (FFT) factor, and image binarization while minutiae extraction consist of ridge thinning process, region of interest (ROI) extraction, and minutiae extraction process. All processes should be done one by one.
Seminar report on Error Handling methods used in bio-cryptographykanchannawkar
Detail information about the real time errors in the biometrics devices and also how to secure encryption keys. To make authentication systems more secure. In this seminar report describe about the combination of the biometrics with the cryptography. and also describe the methods that are used to handle the real time error like fault accept and fault reject and also describe their their rates.i,e FRR and FAR by the biometrics systems.
The researchers have been exploring methods to use biometric characteristics of the user as a replacement for using unforgettable pass-word, in an attempt to build robust cryptographic keys, because, human users detect difficulties to call up long cryptographic keys. Biometric recognition provides an authentic solution to the authentication of the user problem in the identity administration systems. With the extensive utilization of biometric methods in different applications, there is growing concern about the confidentiality and security of the biometric technologies. This paper proposes biometric based key recreation scheme. Since human ears are not correlated. Until now, the encryption keys are generated using a swarm intelligence approach. Collective intelligence of simple groups of autonomous agents have been emerged by swarm intelligence. The crow search algorithm which is known as (CSA) is a new meta-intuitive method assembled by the intelligent group behavior of crows. Despite that CSA demonstrates important features, its search approach poses excessive challenges while faced with great multimodal formularization.
Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authen...IJNSA Journal
Token based security (ID Cards) have been used to restrict access to the Secured systems. The purpose of
Biometrics is to identify / verify the correctness of an individual by using certain physiological or
behavioural traits associated with the person. Current biometric systems make use of face, fingerprints,
iris, hand geometry, retina, signature, palm print, voiceprint and so on to establish a person’s identity.
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport,
pan card, etc. In this paper, we consider face and fingerprint patterns for identification/verification. Using
this data we proposed a novel model for authentication in multimodal biometrics often called ContextSensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for
biometrics fusion patterns. In stage 1, fusion of face and finger patterns using Principal Component
Analysis (PCA), in stage 2 by applying Sparse SVD decomposition to extract the feature patterns from the
fusion data and face pattern and then in stage 3, using CSEAM model, the extracted feature vectors can be
encoded. The final key will be stored in the smart cards as Associative Memory (M), which is often called
Context-Sensitive Associative Memory (CSAM). In CSEAM model, the CSEAM will be computed using
exponential kronecker product for encoding and verification of the chosen samples from the users. The
exponential of matrix can be computed in various ways such as Taylor Series, Pade Approximation and
also using Ordinary Differential Equations (O.D.E.). Among these approaches we considered first two
methods for computing exponential of a feature space. The result analysis of SVD and Sparse SVD for
feature extraction process and also authentication/verification process of the proposed system in terms of
performance measures as Mean square error rates will be presented.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
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Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
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Attacks on counties – USA
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Cyber risk predictions
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
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Length: 30 minutes
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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2.human verification using multiple fingerprint texture 7 20
1. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol 2, No.8, 2011
Human Verification using Multiple Fingerprint Texture
Matchers
Zahoor Ahmad Jhat
1
Department of Electronics, Islamia College of Science and commerce, Srinagar, J&K (India)
zahoorjhat@gmail.com
Ajaz Hussain Mir
Department of Electronics & Communication Engineering, National Institute of Technology, Hazratbal,
Srinagar, J&K (India)
ahmir@rediffmail.com
Seemin Rubab
Department of Physics, National Institute of Technology, Hazratbal, Srinagar, J&K (India)
ask_rubab@yahoo.co.in
Received: 2011-10-23
Accepted: 2011-10-29
Published:2011-11-04
Abstract
This paper presents a multimodal biometric verification system using multiple fingerprint matchers. The
proposed verification system is based on multiple fingerprint matchers using Spatial Grey Level
Dependence Method and Filterbank-based technique. The method independently extract fingerprint
texture features to generate matching scores. These individual normalized scores are combined into a
final score by the sum rule and the final score is eventually used to effect verification of a person as
genuine or an imposter. The matching scores are used in two ways: in first case equal weights are assigned
to each matching scores and in second case user specific weights are used. The proposed verification
system has been tested on fingerprint database of FVC2002. The experimental results demonstrate that the
proposed fusion strategy improves the overall accuracy of the system by reducing the total error rate of the
system.
Keywords: - Multimodal biometric System, Fingerprint verification, SGLDM, Filterbank matching, Score
level fusion, Sum rule.
1. Introduction
In today’s wired information society when our everyday life is getting more and more computerized,
automated security systems are getting more and more importance. The key task for an automated security
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system is to verify that the users are in fact who they claim to be. Traditionally password and ID cards have
been used for human verification to restrict access to secure systems such as ATMs, computers and security
installations [1]. The drawback with the traditional systems is that a password can be guessed or forgotten
and similarly the ID card can be lost or stolen, thus rendering such methods of human verification
unreliable. To overcome these problems, biometrics offers an alternative. Biometrics refers to identifying a
person based on his or her physiological or behavioral traits. Face, fingerprints, hand geometry, iris, retina,
signature, voice, facial thermogram, hand vein, gait, ear, odor, keystroke, etc. are some of the biometric
features that are used for human verification and identification. Most of the biometric systems that are in
use in practical application use a single piece of information for recognition and are as such called
unimodal biometric systems. The unimodal biometric recognition systems, however, have to contend with
a variety of problems like non-universality, susceptibility to spoofing, noise in sensed data, intra-class
variations, inter-class similarities. Some limitations of the unimodal biometric systems can be alleviated
by using multimodal system [2]. A biometric system that combines more than one sources of information
for establishing human identity is called a multimodal biometric system. Combining the information cues
from different biometric sources using an effective fusion scheme can significantly improve accuracy [3] of
a biometric system.
The information fusion in multibiometrics can be done in different ways: fusion at the sensor level,
feature extraction level, matching score level and decision level. Sensor level fusion is rarely used as fusion
at this level requires that the data obtained from the different biometric sensors must be compatible, which
is seldom the case. Fusion at the feature extraction level is not always possible as the feature sets used by
different biometric modalities may either be inaccessible or incompatible. Fusion at the decision level is too
rigid as only a limited amount of information is available. Fusion at the matching score level is, therefore,
preferred due to presence of sufficient information content and the ease in accessing and combining match
scores [4].
2. Related work
A number of works showing advantages of multimodal biometric verification systems have been reported
in literature. Brunelli and Falavigna [2] have proposed personal identification system based on acoustic and
visual features, where they use a HyperBF network as the best performing fusion module. Duc et al. [5]
proposed a simple averaging technique combining face and speech information. Kittler et al. [6] have
experimented with several fusion techniques using face and voice biometrics, including sum, product,
minimum, median, and maximum rules and they have found that the best combination results are obtained
for a simple sum rule. Hong and Jain [7] proposed a multimodal personal identification system which
integrates face and fingerprints that complement each other. The fusion algorithm combines the scores from
the different experts under statistically independence hypothesis. Ben-Yacoub et al. [8] proposed several
fusion approaches, such as Support Vector Machines (SVM), tree classifiers and multi-layer perceptrons,
combining face and voice biometrics. Pigeon at el. [9] proposed a multimodal person authentication
approach based on simple fusion algorithms to combine the results coming from face, and voice biometrics.
Choudhury et al. [10] proposed a multimodal person recognition using unconstrained audio and video and
the combination of the two features is performed using a Bayes net. Jain at el. [11] combine face,
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fingerprint and hand geometry biometrics combining them under sum, decision tree and linear
discriminant- based method. The sum rule is reperted to outperform others. Various other biometric
combinations have been proposed [12, 13, 14] that report that combining more than one biometric
modalities together result in improved performance than using them alone. Jhat et al. [15 ] have proposed
a unimodal fingerprint biometrics verification system using texture feature of Energy of a
fingerprint as a biometric trait that gives 70% Genuine Accept Rate (GAR) at 1% False Accept Rate
(FAR) for effecting personal verification. To augment performance of the said proposed unimodal
fingerprint verification system using a single matching score, in the present work, a multimodal biometric
system based on multiple fingerprint matchers is proposed. The use of the proposed combination strategy in
combining multiple matchers significantly improves the overall accuracy of the fingerprint based
verification system by reducing the total error rates. We have chosen multiple fingerprint matchers as
they form a good combination for a multimodal biometric system because the fusion of this combination in
such systems demonstrates substantial improvement in recognition [3, 6]. It is due to the fact that the
sources are fairly independent [16]. They not only address the problem of non-universality, since multiple
traits ensure sufficient population coverage but also deter spoofing since it would be difficult for an
imposter to spoof multiple biometric traits of a genuine user simultaneously. A multimodal biometric
verification system based on multiple fingerprint matchers is, therefore, described in this paper. To
construct the multimodal biometric verification system, we have combined two fingerprint matchers
of Spatial Grey Level Dependence Method (SGLDM) [27] and Filterbank-based [19] for extracting
matching scores. Such a system has, hitherto, not been tried in the reported literature. The rest of the
paper is arranged as follows: Section 3 describes Fingerprint verification modules. Section 4 presents
normalization of matching scores. Fusion of the normalized scores is addressed in section 5. Experimental
results are shown in section 6 and section 7 concludes the paper.
3. Verification Modules
Fingerprint is the pattern of ridges and valleys on the tip of a finger and is used for personal
verification of people. Fingerprint based recognition method because of its relatively outstanding
features of universality, permanence, uniqueness, accuracy and low cost has made it most popular
and reliable technique. Current fingerprint recognition techniques can be broadly classified as
Minutiae-based, ridge feature-based, correlation-based [17] and gradient based [18]. The
minutiae-based methods are widely used in fingerprint verification but do not utilize a significant
component of the rich discriminatory information available in the ridge structures of the fingerprints.
Further, minutiae-based methods have to contend with the problem of efficiently matching two
fingerprint images containing different numbers of unregistered minutiae points. This is the due to
these reasons that present work uses Texture- based representation of a fingerprint as the smooth flow
pattern of ridges and valleys in a fingerprint can be also viewed as an oriented texture pattern [17].
Texture has been successfully used in extracting hidden information in medical images such as
ultrasound [20], MRI [21], CT [22], retina [23] and Iris [24]. Although there is no strict definition of
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the image texture, however, being defined as a function of the spatial variation in pixel intensities
(grey values), is useful in a variety of applications, e.g, recognition of image regions using texture
properties [25]. Texture methods can be broadly categorized as: statistical, structural, modal, transform
[25, 26]. Teceryan et al [25] and Matreka et al [26] present review of these methods.The two texture
based matchers of Spatial Grey Level Dependence Method (SGLDM) and Filterbank-based, that are
used in the present work for personal verification, are summarized as follows:
3.1 SGLDM- based Matching
Jhat et al. [15] have used Harlicks spatial grey level dependence matrix (SGLDM) [27] method for
extracting statistical texture features. In SGLDM, second order joint conditional probability density
function, f (i, j d , θ ) for directions θ = 0, 45, 90, 135, 180, 225, 270, and 315 degrees is estimated.
Each f (i, j d , θ ) is the probability of going from grey level i to grey level j, given that the
inter-sample spacing is d and the direction is given by the angle θ. The estimated value for these
probability density functions can thus be written in the matrix form:
φ (d , θ ) = [ f (i. j d , θ )] (1)
Scanning of the image in four directions viz; θ = 0, 45, 90, 135 degrees is sufficient for computing
these probability distribution function, as the probability density matrix for the rest of the directions
can be computed from these four basic directions. This yields a square matrix of dimension equal to
the number of intensity levels in the image for each distance d and direction θ. Due to the intensive
nature of computations involved, often only the distances d= 1 and 2 pixels with angles θ = 0, 45, 90,
135 degrees are considered as suggested [26].
Let φ ′(d , θ ) denote transpose of the matrix φ (d , θ ) for the intersampling spacing, d, and direction θ.
φ (d ,0) = φ ′(d ,180)
φ (d ,45) = φ ′(d ,225) (2)
φ (d ,90) = φ ′(d ,270)
φ (d ,135) = φ ′(d ,315)
The knowledge of φ (d ,180), φ (d ,225), φ (d ,270), φ (d ,315) , add nothing to the characterization of
texture. If one chooses to ignore the distinction between opposite directions, then symmetric probability
matrices can be employed and then the spatial grey level dependence matrices
S o (d ), S 45 (d ), S 9 o (d ), S135 (d ) , can be found from
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S o (d ) =
1
[φ (d ,0) + φ (d ,180)] = 1 [φ (d ,0) + φ ′(d ,0)] (3)
2 2
S 45 (d ) = [φ (d ,45) + φ (d ,225)] = [φ (d ,45) + φ ′(d ,45)]
1 1
Similarly
2 2
(4)
S 9 o (d ) and S135 (d ) can be similarly calculated.
Approximately two dozen co-occurrence features can be obtained using the above method and the
consideration of the number of distance angle relations also will lead to a potentially large number of
dependent features. Jhat et al. [15] have shown that the fingerprint texture feature of Energy can provide
useful information for pattern recognition and can be used for verification. The Energy texture feature
of a fingerprint is given by Equation 5.
NG −1NG −1 2
E (Sθ (d )) = ∑ ∑ [Sθ (i, j ) d ]
i =0 j =0
(5)
Where Sθ i, j d ( ) is the (i, j) th element of Sθ (d ) and N G is the number of grey levels in the image
from which the spatial grey level dependence matrices are extracted.
The texture feature of Energy of the fingerprint is calculated using algorithm of SGLDM by taking
d=1 [26], for different values of θ, for a fingerprint being a soft texture [17] require small values of
d. The results of Energy values for the angle of 0, 45, 90 and 135 degrees are obtained as shown in
Figure 1 and are used for discrimination of individuals and effecting personal verification. If the
Euclidean distance between two Energy values of query and gallery fingerprint image is less than a
threshold, then the decision that the two images belong to same finger is made, alternately a decision
that they belong to different fingers is made.
Energy values at angle 0
9010000
8010000
Energy Values
7010000
6010000
5010000
4010000
3010000
2010000
1010000
10000
0 20 40 60 80 100
Fingerprints
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Energy values at angle 45
9010000
Energy Values
8010000
7010000
6010000
5010000
4010000
3010000
2010000
1010000
10000
0 20 40 60 80 100
Fingerprints
Energy values at angle 90
9010000
Energy Values
8010000
7010000
6010000
5010000
4010000
3010000
2010000
1010000
10000
0 20 40 60 80 100
Fingerprints
Energy values at angle 135
9010000
8010000
Energy Values
7010000
6010000
5010000
4010000
3010000
2010000
1010000
10000
0 20 40 60 80 100
Fingerprints
Figure 1. Energy Values for the angles of 0, 45, 90 and 135 degrees.
3.2 Filterbank-based Matching
Jain et al. [19] have proposed a fingerprint representation scheme that utilizes both global and local
features in a compact fixed length feature vector called ‘FingerCode’. The proposed scheme makes use of
the texture features available in a fingerprint to compute the feature vector. In the Filter-based matching,
generic representation of oriented texture relies on extracting a core point in the fingerprint which is
defined as the point of maximum curvature of the ridge in a fingerprint. Then a circular region around the
core point is located and tessellated into sectors. The pixel intensities in each sector are normalized to a
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constant mean and variance and filtered using a bank of 8 Gabor filters to produce a set of 8 filtered images.
Grayscale variance within a sector quantifies the underlying ridge structures and is used as a feature. A
feature vector termed as a FingerCode, is the collection of all the features, computed from all the sectors, in
every filtered image. The FingerCode captures the local information, and the ordered enumeration of the
tessellation captures the invariant global relationships among the local patterns. The matching stage simply
computes the Euclidean distance between the two corresponding FingerCode values. Figure 2 depicts
diagrammatic representation of the Filterbank matching algorithm as proposed by Jain et al. [19].
Figure 2. Diagrammatic representation of Filterbank Matching algorithm.
The first two steps of determining a center point for the fingerprint image and tessellate the
region around the center point are straightforward. The filtering process and obtaining of feature vector can
be summarized as follows:
Let I x, y denote the gray value at pixel x, y in an M N fingerprint image and let M and V ,
3.2.1 Filtering
the estimated mean and variance of sector S , respectively, and N x, y , the normalized gray-level value
at pixel x, y . For all the pixels in sectorS , the normalized image is defined as:
M , if I x, y M
,
N x, y
M ! , otherwise
,
(6)
Where M) and V) are the desired mean and variance values, respectively. The values of both M) and
V) have been set to 100.
An even symmetric Gabor filter has the following general form in the spatial domain:
G x, y; f, Ѳ exp . 1 67 cos 2πfx ,
/ 2 2
0 342 352
(7)
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x ; = x sin Ѳ y cos Ѳ
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y ; = x cos Ѳ ! y sin Ѳ
(8)
Where f is the frequency of the sinusoidal plane wave along the direction Ѳ from the x-axis, and δ 2 and
(9)
δ 2 are the space constants of the Gaussian envelope along x, and y, axes, respectively. Let H indicate the
enhanced image. Convolving H with eight Gabor filters in the spatial domain would be a computationally
F(H) denote the discrete Fourier transform of H, and F GѲ indicate the discrete Fourier transform of the
intensive operation. To speed up this operation the convolution is performed in the frequency domain. Let
Gabor filter having the spatial orientation Ѳ. Then the Gabor filter image,VѲ , may be obtained as,
/ ?F
VѲ = F H F GѲ A
Where F
(10)
/
is the inverse Fourier transform. Eight filtered images are obtained in this way.
3.2.2 Feature vector
Let C Ѳ x, y be the component image corresponding to Ѳ for sector S . For ∀ i ,i=0,1………………,47
The standard deviation within the sectors, in the filter-bank algorithm,, define the feature vector.
(as total of 48 sectors from S) to SCD are defined in six concentric bands around the central point) and
Ѳ ϵ [ 00, 450, 900, 1350] ( as a fingerprint image is decomposed into four components images corresponding
to four different values of Ѳ as mentioned). A feature is standard deviation FiѲ , which is defined as :
F Ѳ = ∑ (C θ ( x , y ) − M θ ) 2
i i
ki
(11)
Where Ki is the number of pixels in Si and MiѲ is the mean of the pixel values in CiѲ(x,y) in sector S . The
average absolute deviations of each sector in each of the eight filtered image define the components of the
feature vector called FingerCode. Fingerprint matching is then based on finding the Euclidean distance
between the corresponding FingerCodes.
4. Normalization.
Normalization involves transforming the raw scores of different modalities to a common domain using
a mapping function. In our case, both the matching scores are distance scores, yet they have different
numerical range. To transform these numerically incompatible matching scores into a common domain
prior to fusion, normalization is needed. Comparing different normalization techniques on different
multimodal biometric systems, Ribaric at el. [28] conclude that no single normalization technique performs
best for all systems. We have, therefore, used min-max technique. This technique is not only simple but
best suited for the case where maximum and minimum values of the scores produced by the matcher are
known. Besides, minimum and maximum scores can be easily shifted to 0 and 1 respectively. The
matching scores are normalized using min-max technique as follows.
Let G represent the gallery templates, Q represent the query samples, and S qg represent the
match score of the particular query ‘q’,qϵQ with gallery template ‘g’, gϵG. Then S qG represents the
vector of scores obtained when a query ‘q’ is matched against the entire gallery G. In min-max
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normalization, the minimum and the maximum of this score vector are used to obtain the normalization
score S' qg as per Equation12. The normalized score lie in the range 0-1.
S qg − min(S qG )
S ' qg =
max(S qG ) − min(S P G )
(12)
5. Fusion.
The matching scores, next to feature vectors, output by matchers contain the richest information [4]
about the input pattern. Further, it is relatively easy to access and combine the scores generated by the
different matchers. Consequently, integration of information at the matching score level is the most
common approach in the multimodal biometric systems. The proposed method, therefore, fuses the
individual match scores of the fingerprint and the fused score is used for verification. There are several
classifiers for the fusion and analysis of several classifier rules is given in [6, 11]. It is suggested that the
weighted sum rule is more effective and outperforms other fusion strategies based on empirical
N
observations [29]. The weighted sum rule is defined as S fusion = ∑ Wi S i Where S i is the normalized
i =1
matching score provided by the i th trait and W i is the weight assigned to the i th trait. The identity of a
person is verified if S fusion ≥ η , where η is the matching threshold. The weighting of the matching
scores has been done in the following ways:
5.1 Weighing Matching Scores Equally
In the first experiment, equal weightage is given to two matching scores of a fingerprint and a new
2
1
score is obtained. Then the final matching score S fusion = ∑ S i is compared against a certain
i =1 2
threshold value to make a decision for a person being genuine or an imposter. The Figure 3a shows the
improved matching performance when equal weightage is given to both matching scores of the
fingerprint.
5.2 Weighing Matching Scores Unequally
When biometric trait of a user cannot be reliably acquired, the user will experience high false reject
rate. This can result when the biometric trait becomes unreadable due to dirty or worn down dry fingers. In
such a situation, the false error rate can be reduced and accuracy improved if different matching scores are
weighted differently for increasing the influence of one or the other matching score as per degree of
importance for different users. Weights indicate the importance of individual biometric matchers in a
multibiometric system and, therefore, the set of weights are determined for a specific user such that the
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total error rates corresponding to that user can be minimized. User specific weights are estimated [29] from
the training data as follows:
1. For the i th user in the database, vary weights W1,i , and W2 ,i over the range [ 0,1], with
the condition W1,i + W2,i =1
2. Compute S fusion = W1,i S1 + W2,i S 2
3. Choose that set of weights that minimizes the total error rate associated with the scores. The total
error rate is sum of the false accept and false reject rates.
The user specific weight procedure utilizes the histograms of both the genuine and imposter score and
computing user-specific thresholds using imposter scores have been shown not to improve performance [30]
very much. In the second experiment, with a common threshold, therefore, we assign different weights
to matching scores to minimize false accept rate and false reject rate associated with an individual and
improve further the matching performance. The improved matching performance when user specific
weights are used, is shown in Figure 3b.
6. Experimental Results
The suggested method has been tested on fingerprint databases of FVC2002 DB1 and DB2 [31]. Both
the databases contain images of 110 different fingers with 8 impressions for each finger yielding a total of
880 fingerprints in each database. The databases has been divided into two sets: A and B. Set A
contains the fingerprint images from the first 100 fingers as evaluation set and Set B contains the
remaining 10 fingers as a training set. About 10 fingerprint images were eliminated from the database as
Filter-based matcher rejected the images either being of poor quality or failing to locate the center. The
False Accept Rate (FAR) and False Reject Rate (FRR) for the suggested method were evaluated by using
the protocols of FVC2002 [32]. Each fingerprint impression in the subset A is matched against the
remaining impressions of the same finger to compute genuine distribution. The total genuine attempts is
(8×7)/2×90 = 2520. For Imposter distribution, the first fingerprint impression of each finger in subset A is
matched against the first impression of the remaining fingers. The total imposter attempts is (90×89)/2 =
4005. The normalized genuine and imposter distribution matching scores for DB1 and DB2 are shown in
Figures 4(a) and (b) respectively.
For the multiple matcher combination, we randomly selected each of the genuine and imposter
scores for the training and remaining each half for the test. This process has been repeated 5 times to give 5
different training sets and 5 corresponding independent test sets. For authentication, we randomly selected
four impressions of each fingerprint and enrolled them as templates into the system database. The
remaining 90 × 4 = 360 fingerprints images in each database were used as input fingerprints to test the
performance of our proposed method. The matching scores of the two classifiers are then summed and the
final matching score is compared against a certain threshold value to recognize the person as genuine or an
imposter. The FAR and FFR rates with different threshold values were obtained based on 90 × 360 = 32400
matches in each database.
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False Accept Rate False Reject Rate
FAR (%) FRR (%)
SGLDM Filter SGLDM +Filter
1 19.8 15.3 4.9
.1 34.5 26.0 13.8
.01 39.4 32.1 17.3
Table 1. False Reject Rates ( FRR) with different values of False accept rates (FAR) when matching
scores are equally weighted.
False Accept Rate False Reject Rate
FAR (%) FRR (%)
SGLDM Filter SGLDM +Filter
1 18.2 14.5 3.8
.1 33.2 24.9 12.7
.01 37.8 30.9 15.5
Table 2. False Reject Rates ( FRR) with different values of False accept rates (FAR) when matching scores
are unequally weighted.
To demonstrate the effectiveness of the proposed method, tables showing FAR and FRR are drawn in Table
1 and Table 2. Besides, ROC curves between FAR and GAR have also been plotted in Figure 3a and Figure
3b. It is evident from the ROC curves that performance gain obtained for the proposed fusion system
is higher as compared to the performance obtained for two individual matchers. As shown in the
Figures 3a and 3b, the integration of matchers enhances the performance of the proposed multimodal
verification system over the unimodal fingerprint matcher as proposed in [15] by giving Genuine Accept
Rate ( GAR ) of 95.1% and 96.2% respectively at False Accept Rate ( FAR ) of 1%.
7. Conclusion
A biometric verification system using a single fingerprint texture matcher is less accurate for
effecting personal verification. To enhance the performance of such a unimodal verification system, a
multimodal biometric verification system using multiple fingerprint matchers is proposed. The proposed
verification system use Spatial Grey Level Dependence Method (SGLDM) and Filterbank-based matching
technique to independently extract fingerprint texture features to generate matching scores. These
individual normalized scores are combined into a final score by the sum rule. The matching scores are used
in two ways, in first case equal weights are assigned to each matching scores and in second case user
specific weights are used. The final fused score is eventually used to conclude a person as genuine or an
imposter. The proposed verification system has been tested on fingerprint database of FVC2002. The
experimental results demonstrate that the proposed fusion strategy improves the overall accuracy of the of
the unimodal biometric verification system by reducing the total error rate of the system.
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SGLDM
Genuine Accept Rate (%)
Filter
SGLDM + Filter
False Accept Rate (%)
Figure 3. ROC curves showing performance improvement of combination of matchers over individual
matchers when matching scores are (a) weighted equally (b) weighted unequally.
Genuine Imposter
Genuine
Percentage
Percentage
Threshold Threshold
(a) (b)
Figure 4. Genuine and Imposter distributions for (a) DB1, (b) DB2.
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