This document provides an overview of multimodal biometrics, including a review of applications, challenges, and research areas. It discusses combining multiple biometric traits like face and fingerprint to improve authentication accuracy compared to single biometrics. Two main approaches for multimodal biometrics are multi-algorithm, which uses different algorithms on a single sample, and multi-sample, which takes multiple samples of the same trait. Challenges include developing consistent high-quality sensors and addressing privacy and standardization issues. Potential research areas focus on improving matching algorithms, sensor integration, and analyzing the scalability of multimodal biometric systems.
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.
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.
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.
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.
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.
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.
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.
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.
Feature selection for multiple water quality status: integrated bootstrapping...IJECEIAES
STORET is one method to determine the river water quality, and to classify them into four classes (very good, good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows, the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accuracy from 83.3% to be 98.8%. While the process of noise elimination onthe data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).
Multibiometric Secure Index Value Code Generation for Authentication and Retr...ijsrd.com
The use of multiple biometric sources for human recognition, referred to as multibiometrics, mitigates some of the limitations of unimodal biometric systems by increasing recognition accuracy, improving population coverage, imparting fault-tolerance, and enhancing security. In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). An alternative e approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The number of multibiometric systems deployed on a national scale is increasing and the sizes of the underlying databases are growing. These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities. Searching a biometric database for an identity is usually done by comparing the probe image against every enrolled identity in the database and generating a ranked list of candidate identities. Depending on the nature of the matching algorithm, the matching speed in some systems can be slow. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification. New representation schemes that allow for faster search and, therefore, shorter response time are needed.
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.
Machine Learning Based Approaches for Cancer Classification Using Gene Expres...mlaij
The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
Trust Enhanced Role Based Access Control Using Genetic Algorithm IJECEIAES
Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
Ijaems apr-2016-1 Multibiometric Authentication System Processed by the Use o...INFOGAIN PUBLICATION
The present day authentication system is mostly uni-model i.e having only single authentication method which can be either finger print, iris , palm veins ,etc. Thus these models have to contend with a variety of problems such as absurd or unusual data, non-versatility; un authorized attempts, and huge amount of error rates. Some of these limitations can be reduced or stopped by the use of multimodal biometric systems that integrate the evidence presented by several sources of information. This paper converses a multi biometric based authentication system based on Fusion algorithm using a key. Our work mainly focuses on the fusion algorithm, i.e fusion of finger and palm print out of which the greatest features from the above two traits are taken into account. With minimum possible features the fusion of the both the traits is carried out. Then some key is generated for multi biometric authentication. By processing the test image of a person, the identity of the person is displayed with his/her own image. By the fusion algorithm, it is found that it has less computation time compared to the existing algorithms. By matching results, we validate and authenticate the particular individual.
The classification of different types of tumors is of great importance in cancer diagnosis and its drug discovery. Cancer classification via gene expression data is known to contain the keys for solving the fundamental problems relating to the diagnosis of cancer. The recent advent of DNA microarray technology has made rapid monitoring of thousands of gene expressions possible. With this large quantity of gene expression data, scientists have started to explore the opportunities of classification of cancer using a gene expression dataset. To gain a profound understanding of the classification of cancer, it is necessary to take a closer look at the problem, the proposed solutions, and the related issues altogether. In this research thesis, I present a new way for Leukemia classification using the latest AI technique of Deep learning using Google TensorFlow on gene expression data.
Indexing of bio metric databases:what is bio-metric?
What is indexing?
How indexing works on bio-metric databases?
Uses of indexing in databases, Indexing types etc.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
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.
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.
Feature selection for multiple water quality status: integrated bootstrapping...IJECEIAES
STORET is one method to determine the river water quality, and to classify them into four classes (very good, good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows, the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accuracy from 83.3% to be 98.8%. While the process of noise elimination onthe data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).
Multibiometric Secure Index Value Code Generation for Authentication and Retr...ijsrd.com
The use of multiple biometric sources for human recognition, referred to as multibiometrics, mitigates some of the limitations of unimodal biometric systems by increasing recognition accuracy, improving population coverage, imparting fault-tolerance, and enhancing security. In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). An alternative e approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The number of multibiometric systems deployed on a national scale is increasing and the sizes of the underlying databases are growing. These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities. Searching a biometric database for an identity is usually done by comparing the probe image against every enrolled identity in the database and generating a ranked list of candidate identities. Depending on the nature of the matching algorithm, the matching speed in some systems can be slow. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification. New representation schemes that allow for faster search and, therefore, shorter response time are needed.
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.
Machine Learning Based Approaches for Cancer Classification Using Gene Expres...mlaij
The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
Trust Enhanced Role Based Access Control Using Genetic Algorithm IJECEIAES
Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
Ijaems apr-2016-1 Multibiometric Authentication System Processed by the Use o...INFOGAIN PUBLICATION
The present day authentication system is mostly uni-model i.e having only single authentication method which can be either finger print, iris , palm veins ,etc. Thus these models have to contend with a variety of problems such as absurd or unusual data, non-versatility; un authorized attempts, and huge amount of error rates. Some of these limitations can be reduced or stopped by the use of multimodal biometric systems that integrate the evidence presented by several sources of information. This paper converses a multi biometric based authentication system based on Fusion algorithm using a key. Our work mainly focuses on the fusion algorithm, i.e fusion of finger and palm print out of which the greatest features from the above two traits are taken into account. With minimum possible features the fusion of the both the traits is carried out. Then some key is generated for multi biometric authentication. By processing the test image of a person, the identity of the person is displayed with his/her own image. By the fusion algorithm, it is found that it has less computation time compared to the existing algorithms. By matching results, we validate and authenticate the particular individual.
The classification of different types of tumors is of great importance in cancer diagnosis and its drug discovery. Cancer classification via gene expression data is known to contain the keys for solving the fundamental problems relating to the diagnosis of cancer. The recent advent of DNA microarray technology has made rapid monitoring of thousands of gene expressions possible. With this large quantity of gene expression data, scientists have started to explore the opportunities of classification of cancer using a gene expression dataset. To gain a profound understanding of the classification of cancer, it is necessary to take a closer look at the problem, the proposed solutions, and the related issues altogether. In this research thesis, I present a new way for Leukemia classification using the latest AI technique of Deep learning using Google TensorFlow on gene expression data.
Indexing of bio metric databases:what is bio-metric?
What is indexing?
How indexing works on bio-metric databases?
Uses of indexing in databases, Indexing types etc.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
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.
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.
Multi-modal palm-print and hand-vein biometric recognition at sensor level fu...IJECEIAES
When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including k-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.
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 authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
framework for providing more secure key in biometric security aspect. Initially the features will be
extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
and then verified with the measure MSE. This model would give better outcome when compared with SVD
factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
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
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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
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Review of Multimodal Biometrics: Applications, Challenges and Research Areas
1. Prof. V. M. Mane and Prof. (Dr.) D. V. Jadhav
International Journal of Biometrics and Bioinformatics (IJBB), Volume 3, Issue 5 90
Review of Multimodal Biometrics: Applications, challenges
and Research Areas
Prof. Vijay M. Mane manevijaym@rediffmail.com
Assistant Professor
Department of Electronics Engineering,
Vishwakarma Institute of Technology, Pune (India)
Prof. (Dr.) Dattatray V. Jadhav dvjadhao@yahoo.com
Professor
Department of Electronics Engineering,
Vishwakarma Institute of Technology, Pune (India)
Abstract
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.
Keywords: Multimodal, biometrics, feature extraction, spoofing.
1. INTRODUCTION
Biometrics refers to the physiological or behavioral characteristics of a person to authenticate
his/her identity [1]. The increasing demand of enhanced security systems has led to an
unprecedented interest in biometric based person authentication system. Biometric systems
based on single source of information are called unimodal systems. Although some unimodal
systems [2] have got considerable improvement in reliability and accuracy, they often suffer
from enrollment problems due to non-universal biometrics traits, susceptibility to biometric
spoofing or insufficient accuracy caused by noisy data [3].
Hence, single biometric may not be able to achieve the desired performance requirement in
real world applications. One of the methods to overcome these problems is to make use of
multimodal biometric authentication systems, which combine information from multiple
modalities to arrive at a decision. Studies have demonstrated that multimodal biometric
systems can achieve better performance compared with unimodal systems.
This paper presents the review of multimodal biometrics. This includes applications,
challenges and areas of research in multimodal biometrics. The different fusion techniques of
multimodal biometrics have been discussed. The paper is organized as follows. Multi
algorithm and multi sample approach is discussed in Section 2 whereas need of multimodal
biometrics is illustrated in Section 3, the review of related work, different fusion techniques are
presented in Section 4. Applications, challenges and research areas are given in Section 5
and Section 6 respectively. Conclusions are presented in the last section of the paper.
2. MULTI ALGORITHM AND MULTI SAMPLE APPROACH
Multi algorithm approach employs a single biometric sample acquired from single sensor. Two
or more different algorithms process this acquired sample. The individual results are
combined to obtain an overall recognition result. This approach is attractive, both from an
2. Prof. V. M. Mane and Prof. (Dr.) D. V. Jadhav
International Journal of Biometrics and Bioinformatics (IJBB), Volume 3, Issue 5 91
application and research point of view because of use of single sensor reducing data
acquisition cost. The 2002 Face Recognition Vendor Test has shown increased performance
in 2D face recognition by combining the results of different commercial recognition systems
[4]. Gokberk et al. [5] have combined multiple algorithms for 3D face recognition. Xu et al. [6]
have also combined different algorithmic approaches for 3D face recognition.
Multi sample or multi instance algorithms use multiple samples of the same biometric. The
same algorithm processes each of the samples and the individual results are fused to obtain
an overall recognition result. In comparison to the multi algorithm approach, multi sample has
advantage that using multiple samples may overcome poor performance due to one sample
that has unfortunate properties. Acquiring multiple samples requires either multiple copies of
the sensor or the user availability for a longer period of time. Compared to multi algorithm,
multi sample seems to require either higher expense for sensors, greater cooperation from
the user, or a combination of both. For example, Chang et al. [7] used a multi-sample
approach with 2D face images as a baseline against which to compare the performance of
multi-sample 2D + 3D face.
3. NEED OF MULTIMODAL BIOMETRICS
Most of the biometric systems deployed in real world applications are unimodal which rely on
the evidence of single source of information for authentication (e.g. fingerprint, face, voice
etc.). These systems are vulnerable to variety of problems such as noisy data, intra-class
variations, inter-class similarities, non-universality and spoofing. It leads to considerably high
false acceptance rate (FAR) and false rejection rate (FRR), limited discrimination capability,
upper bound in performance and lack of permanence [8]. Some of the limitations imposed by
unimodal biometric systems can be overcome by including multiple sources of information for
establishing identity. These systems allow the integration of two or more types of biometric
systems known as multimodal biometric systems. These systems are more reliable due to the
presence of multiple, independent biometrics [9]. These systems are able to meet the
stringent performance requirements imposed by various applications. They address the
problem of non-universality, since multiple traits ensure sufficient population coverage. They
also deter spoofing since it would be difficult for an impostor to spoof multiple biometric traits
of a genuine user simultaneously. Furthermore, they can facilitate a challenge – response
type of mechanism by requesting the user to present a random subset of biometric traits
thereby ensuring that a ‘live’ user is indeed present at the point of data acquisition.
4. MULTIMODAL BIOMETRICS
The term “multimodal” is used to combine two or more different biometric sources of a person
(like face and fingerprint) sensed by different sensors. Two different properties (like infrared
and reflected light of the same biometric source, 3D shape and reflected light of the same
source sensed by the same sensor) of the same biometric can also be combined. In
orthogonal multimodal biometrics, different biometrics (like face and fingerprint) are involved
with little or no interaction between the individual biometric whereas independent multimodal
biometrics processes individual biometric independently. Orthogonal biometrics are
processed independently by necessity but when the biometric source is the same and
different properties are sensed, then the processing may be independent, but there is at least
the potential for gains in performance through collaborative processing. In collaborative
multimodal biometrics the processing of one biometric is influenced by the result of another
biometric.
A generic biometric system has sensor module to capture the trait, feature extraction module
to process the data to extract a feature set that yields compact representation of the trait,
classifier module to compare the extracted feature set with reference database to generate
matching scores and decision module to determine an identity or validate a claimed identity.
In multimodal biometric system information reconciliation can occur at the data or feature
level, at the match score level generated by multiple classifiers pertaining to different
modalities and at the decision level.
3. Prof. V. M. Mane and Prof. (Dr.) D. V. Jadhav
International Journal of Biometrics and Bioinformatics (IJBB), Volume 3, Issue 5 92
Biometric systems that integrate information at an early stage of processing are believed to
be more effective than those which perform integration at a later stage. Since the feature set
contains more information about the input biometric data than the matching score or the
output decision of a matcher, fusion at the feature level is expected to provide better
recognition results. However, fusion at this level is difficult to achieve in practice because the
feature sets of the various modalities may not be compatible and most of the commercial
biometric systems do not provide access to the feature sets which they use. Fusion at the
decision level is considered to be rigid due to the availability of limited information. Thus,
fusion at the match score level is usually preferred, as it is relatively easy to access and
combine the scores presented by the different modalities [1].
Rukhin and Malioutov [10] proposed fusion based on a minimum distance method for
combining rankings from several biometric algorithms. Fusion methods were compared by
Kittler et al. [11], Verlinde et al. [12] and Fierrez-Aguilar et al. [13]. Kittler found that the sum
rule outperformed many other methods, while Fierrez-Aguilar et al. [13, 14] and Gutschoven
and Verlinde [15] designed learning based strategies using support vector machines.
Researchers have also investigated the use of quality metrics to further improve the
performance [16, 14, 17–21].
Many of these techniques require the scores for different modalities (or classifiers) to be
normalized before being fused and develop weights for combining normalized scores.
Normalization and quality weighting schemes involve assumptions that limit the applicability of
the technique. In [22], Bayesian belief network (BBN) based architecture for biometric fusion
applications is proposed. Bayesian networks provide united probabilistic framework for
optimal information fusion. Although Bayesian methods have been used in biometrics [16,
23–25], the power and flexibility of the BBN has not been fully exploited.
Brunelli et al. [26] used the face and voice traits of an individual for identification. A Hyper BF
network is used to combine the normalized scores of five different classifiers operating on the
voice and face feature sets. Bigun et al. [16] developed a statistical framework based on
Bayesian statistics to integrate the speech (text dependent) and face data of a user [27]. The
estimated biases of each classifier are taken into account during the fusion process. Hong
and Jain associate different confidence measures with the individual matchers when
integrating the face and fingerprint traits of a user [28]. They also suggest an indexing
mechanism wherein face information is used to retrieve a set of possible identities and the
fingerprint information is then used to select a single identity. A commercial product called
BioID [29] uses the voice, lip motion and face features of a user to verify the identity. Aloysius
George used Linear Discriminant analysis (LDA) for face recognition and Directional filter
bank (DFB) for fingerprint matching. Based on experimental results, the proposed system
reduces FAR down to 0.0000121%, which overcomes the limitation of single biometric system
and proves stable personal verification in real-time [30].
5. APPLICATIONS
The defense and intelligence communities require automated methods capable of rapidly
determining an individual’s true identity as well as any previously used identities and past
activities, over a geospatial continuum from set of acquired data. A homeland security and
law enforcement community require technologies to secure the borders and to identify
criminals in the civilian law enforcement environment. Key applications include border
management, interface for criminal and civil applications, and first responder verification.
Enterprise solutions require the oversight of people, processes and technologies. Network
infrastructure has become essential to functions of business, government, and web based
business models. Consequently securing access to these systems and ensuring one’s
identity is essential. Personal information and Business transactions require fraud prevent
solutions that increase security and are cost effective and user friendly. Key application areas
include customer verification at physical point of sale, online customer verification etc.
6. CHALLENGES AND RESEARCH AREAS
4. Prof. V. M. Mane and Prof. (Dr.) D. V. Jadhav
International Journal of Biometrics and Bioinformatics (IJBB), Volume 3, Issue 5 93
Based on applications and facts presented in the previous sections, followings are the
challenges in designing the multi modal systems. Successful pursuit of these biometric
challenges will generate significant advances to improve safety and security in future
missions. The sensors used for acquiring the data should show consistency in performance
under variety of operational environment. Fundamental understanding of biometric
technologies, operational requirements and privacy principles to enable beneficial public
debate on where and how biometrics systems should be used, embed privacy functionality
into every layer of architecture, protective solutions that meet operational needs, enhance
public confidence in biometric technology and safeguard personal information.
Designing biometric sensors, which automatically recognize the operating environment
(outdoor / indoor / lighting etc) and communicate with other system components to
automatically adjust settings to deliver optimal data, is also the challenging area. The sensor
should be fast in collecting quality images from a distance and should have low cost with no
failures to enroll [IJBB5].
The multimodal biometric systems can be improved by enhancing matching algorithms,
integration of multiple sensors, analysis of the scalability of biometric systems, followed by
research on scalability improvements and quality measures to assist decision making in
matching process. Open standards for biometric data interchange formats, file formats,
applications interfaces, implementation agreements, testing methodology, adoption of
standards based solutions, guidelines for auditing biometric systems and records and
framework for integration of privacy principles are the possible research areas in the field.
7. CONCLUSIONS
This paper presented the various issues related to multimodal biometric systems. By
combining multiple sources of information, the improvement in the performance of biometric
system is attained. Various fusion levels and scenarios of multimodal systems are discussed.
Fusion at the match score level is the most popular due to the ease in accessing and
consolidating matching scores. Performance gain is pronounced when uncorrelated traits are
used in a multimodal system. The challenges faced by multimodal biometric system and
possible research areas are also discussed in the paper.
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