This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
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.
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.
Palmprint and Handgeometry Recognition using FAST features and Region propertiesEditor IJCATR
Biometrics recognition system is more reliable and popular. In this paper we describe a palmprint and handgeometry based person
identification consisting of three main steps - preprocessing techniques such as morphological operations. The feature extraction techniques
such as FAST feature algorithm and region properties is used to independently extract palmprint and handgeometry features. Feature matching
with euclidean distance classifier. These techniques are more reliable and faster than traditional techniques used. We finally conclude that the
proposed methodology has better performance .This is supported by our experiments which are able to achieve recognition rate for palmprint
100 % and for handgeometry 93.75 %.
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.
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
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.
A review on fake biometric detection system for various applicationseSAT Journals
Abstract Now a days Security is a major concern for Scenario. So many securities are available but it should be reliable. A biometric system is a computer system which is related to the human characteristic. It is mainly used in identification and access control on their behavioral and physiological category. For example signature, voice, retina, key stroke, face, iris and fingerprint etc. This paper introduces a software base multi attack protection method which is based on various biometric modalities such as iris, face, signature and hand palm image.. This Hand palm technique is used for physical access. The real and fake images are identified by using image quality assessment (IQA) technique. Fake identities always have some different feature than original such as sharpness, different color, information quality etc. In this paper, liveness detection method is used. Which provide a very good performance and low degree of complexity. Also quality of Image is using two methods Full- Reference (FR) and No-Reference (NR). This image quality assessment (IQA) method is suitable for real time application which has been used for very low complexity. Keyword: Statistical Feature Extraction Biometric, Attack, Image Quality Assessment, Full-reference IQA, NO-Reference IQA,.
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.
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.
Palmprint and Handgeometry Recognition using FAST features and Region propertiesEditor IJCATR
Biometrics recognition system is more reliable and popular. In this paper we describe a palmprint and handgeometry based person
identification consisting of three main steps - preprocessing techniques such as morphological operations. The feature extraction techniques
such as FAST feature algorithm and region properties is used to independently extract palmprint and handgeometry features. Feature matching
with euclidean distance classifier. These techniques are more reliable and faster than traditional techniques used. We finally conclude that the
proposed methodology has better performance .This is supported by our experiments which are able to achieve recognition rate for palmprint
100 % and for handgeometry 93.75 %.
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.
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
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.
A review on fake biometric detection system for various applicationseSAT Journals
Abstract Now a days Security is a major concern for Scenario. So many securities are available but it should be reliable. A biometric system is a computer system which is related to the human characteristic. It is mainly used in identification and access control on their behavioral and physiological category. For example signature, voice, retina, key stroke, face, iris and fingerprint etc. This paper introduces a software base multi attack protection method which is based on various biometric modalities such as iris, face, signature and hand palm image.. This Hand palm technique is used for physical access. The real and fake images are identified by using image quality assessment (IQA) technique. Fake identities always have some different feature than original such as sharpness, different color, information quality etc. In this paper, liveness detection method is used. Which provide a very good performance and low degree of complexity. Also quality of Image is using two methods Full- Reference (FR) and No-Reference (NR). This image quality assessment (IQA) method is suitable for real time application which has been used for very low complexity. Keyword: Statistical Feature Extraction Biometric, Attack, Image Quality Assessment, Full-reference IQA, NO-Reference IQA,.
User verification systems that use a single source of biometric information are not sufficient to meet today’s high security requirements for applications. This is because these systems have to contend with noisy data, intra-class variations, spoof attack and non-universality. Therefore, there is need for employing multiple sources of biometric information to provide better recognition performance as compared to the systems based on single trait. This paper is an overview of different categories of multibiometric systems, information fusion in multibiometric systems, and approaches to feature fusion at feature selection phase.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
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.
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
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.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,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. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens 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. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
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.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
Existing definitions for biometric testing and
evaluation do not fully explain errors in a biometric system. This paper provides a definitional framework for the Human
Biometric-Sensor Interaction (HBSI) model. This paper proposes six new definitions based around two classifications of
presentations, erroneous and correct. The new terms are: defective interaction (DI), concealed interaction (CI), false interaction (FI), failure to detect (FTD), failure to extract
(FTX), and successfully acquired samples (SAS). As with all definitions, the new terms require a modification to the general biometric model.
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.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
The purpose of this paper is to extend the Human
Biometric Sensor Interaction (HBSI) model to various
modalities, in this case, hand geometry. As the data was
collected at different times, there was a slight modification in
training between group 1 and group 2. Therefore, a secondary
purpose of this paper was to examine the differences in the
HBSI metrics when individuals are given two different types of
training (one using video training, and the other using small
group lecture-style training). 151 individuals were asked to
perform an enrollment transaction and three successive postenrollment
verification attempts with the hand geometry
machine, and an observational analysis was performed on
their interactions. This type of analysis is novel to the field of
biometrics and the human interaction component has only
recently received attention [1]. Using a framework developed
specifically for studying various human interaction errors, the
observations from hand recognition device placements were
analyzed and mapped onto the HBSI error framework. Instead
of categorizing a user error as a failure to enroll (FTE) or
failure to acquire (FTA), a more comprehensive categorization
of these errors were developed. Both incorrect and correct
interaction errors were coded and binned in appropriate
categories by a human observer. The results showed that
hand geometry modality could fit the existing HBSI model.
Furthermore, the experiment highlighted slight variations in
errors due to training, which will be investigated further in
another paper.
User verification systems that use a single source of biometric information are not sufficient to meet today’s high security requirements for applications. This is because these systems have to contend with noisy data, intra-class variations, spoof attack and non-universality. Therefore, there is need for employing multiple sources of biometric information to provide better recognition performance as compared to the systems based on single trait. This paper is an overview of different categories of multibiometric systems, information fusion in multibiometric systems, and approaches to feature fusion at feature selection phase.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
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.
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
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.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,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. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens 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. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
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.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
Existing definitions for biometric testing and
evaluation do not fully explain errors in a biometric system. This paper provides a definitional framework for the Human
Biometric-Sensor Interaction (HBSI) model. This paper proposes six new definitions based around two classifications of
presentations, erroneous and correct. The new terms are: defective interaction (DI), concealed interaction (CI), false interaction (FI), failure to detect (FTD), failure to extract
(FTX), and successfully acquired samples (SAS). As with all definitions, the new terms require a modification to the general biometric model.
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.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
The purpose of this paper is to extend the Human
Biometric Sensor Interaction (HBSI) model to various
modalities, in this case, hand geometry. As the data was
collected at different times, there was a slight modification in
training between group 1 and group 2. Therefore, a secondary
purpose of this paper was to examine the differences in the
HBSI metrics when individuals are given two different types of
training (one using video training, and the other using small
group lecture-style training). 151 individuals were asked to
perform an enrollment transaction and three successive postenrollment
verification attempts with the hand geometry
machine, and an observational analysis was performed on
their interactions. This type of analysis is novel to the field of
biometrics and the human interaction component has only
recently received attention [1]. Using a framework developed
specifically for studying various human interaction errors, the
observations from hand recognition device placements were
analyzed and mapped onto the HBSI error framework. Instead
of categorizing a user error as a failure to enroll (FTE) or
failure to acquire (FTA), a more comprehensive categorization
of these errors were developed. Both incorrect and correct
interaction errors were coded and binned in appropriate
categories by a human observer. The results showed that
hand geometry modality could fit the existing HBSI model.
Furthermore, the experiment highlighted slight variations in
errors due to training, which will be investigated further in
another paper.
Advanced Authentication Scheme using Multimodal Biometric SchemeEditor IJCATR
Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today‟s biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.
This is a complete report on Bio-metrics, finger print detection. It include what finger print is, how to scan and refin finger print, how the mechanism of its detection work, applications, etc
A hybrid learning scheme towards authenticating hand-geometry using multi-mo...IJECEIAES
Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.
A Biometric Fusion Based on Face and Fingerprint Recognition using ANNrahulmonikasharma
Biometric systems are used for identifying and recognizing individual characteristics on the basis of biological or behavioral features. In the research work, a biometric fusion system based on fingerprint and face using the artificial intelligence technique is proposed. To achieve better accuracy of the biometric fusion system, the uniqueness of feature is significant. To find out the unique feature set from the data, we have used different feature extraction algorithm in the proposed biometric fusion system. Initially, pre-processing has been applied on the test images which is used to remove the unwanted data from the uploaded image and return an appropriate data for further process. In the fingerprint part, minutia extraction is used as a feature of fingerprint whereas Extended Local Binary pattern (ELBP) is used for extracting features of face and creates a pattern of face features. To create a unique feature set, optimization algorithm is needed and we have used genetic algorithm as a feature optimization technique. In the proposed fusion system, ANN is used to classify the test data according to the trained ANN structure with optimized feature data of fingerprint and face. To check the efficiency of proposed fusion system, we have calculated the performance parameters like FAR, FRR and Accuracy. From the analysis of proposed fusion system, we have observed that the accuracy of the proposed work is better than the previous ones and it is more than the 94%. To design a proposed biometric fusion system, image processing toolbox is used under the MATLAB environment.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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
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.
Intelligent multimodal identification system based on local feature fusion be...nooriasukmaningtyas
Biometric identification systems, which use physical features to check a person's identity, ensure much higher security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single modal biometric identification system fails to extract enough features for identification. Another disadvantage of using only one feature is not always readable. In this article, a smart multimodal biometric verification model for identifying and verifying a person's identity is recommended based on artificial intelligence methods. The proposed model is identified the iris and finger vein unique patterns each individual to overcome many challenges such as identity fraud, poor image quality, noise, and instability of the surrounding environment. Several experiments were performed on a dataset containing 50 people by using many matching methods. The results of the proposed model were provided a higher accuracy of 98%, with FAR and FRR of 0.0015% and 0.025%, respectively.
Biometrics is the science and technology of
measuring and analyzing biological data. In information
technology, biometrics refers to technologies that measure and
analyze human body characteristics, such as DNA, fingerprints,
eye retinas and irises, voice patterns, facial patterns and hand
measurements, for authentication purposes. This paper is about
the applications of biometric especially in the field of healthcare
and its future uses
A Fast and Accurate Palmprint Identification System based on Consistency Orie...IJTET Journal
Abstract — A palmprint identification system is a relatively most promising physiological biometric approach to identify the person. The numbers of palmprint recognition based biometric system have been successfully applied for real world access to control applications. A typical palmprint identification system identifies a query palmprint and matching it with the template stored in the database and comparing the similarity score with a pre-defined threshold. The Consistency Orientation Pattern (COP) hashing method is implemented in this work to enforce the fast search and to obtain the accurate result. Orientation pattern (OP) is defined as a collection of orientation features at arbitrary positions. The principal palm line is a kind of evident and stable features in palmprint images, and the orientation features in this region are expected to be more consistent than others. Using the orientation and response features extracted by steerable filter and gives an analysis on the consistency of orientation features, and then introduces a method to construct COP using the consistent features. Those features can be used as the indexes to the target template. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly. This method can lead to early termination of the searching process.
Verification or Authentication systems use a single biometric sensor which having higher error rate due to single evidence of identity (voice can be change due to cold, face can be changed due facial hairs, cosmetics, fingerprint can be change due to scar etc.). To enhance the performance of single biometric systems in these situations may not be effective because of these problems. Multi-biometric systems overcome some of these limitations by providing multiple proofs of any identity. This paper presents an effective multimodal biometric system which can be used to reduce the above mentioned drawbacks of unimodal systems.
BIOMETRIC BASED AUTHENTICATION SYSTEM TECHNOLOGY
JMeter webinar - integration with InfluxDB and GrafanaRTTS
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.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
A NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO ENHANCE PERFORMANCE OF BIOMETRIC IDENTIFICATION SYSTEM
1. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
DOI:10.5121/ijcsa.2015.5502 13
A NOVEL BINNING AND INDEXING APPROACH
USING HAND GEOMETRY AND PALM PRINT TO
ENHANCE PERFORMANCE OF BIOMETRIC
IDENTIFICATION SYSTEM
Anitha M L and Dr RadhaKrishna Rao K A
P .E .S .College Of Engineering,Mandya, Karnataka, India
ABSTRACT
This paper proposes a Biometric identification system for person identification using two biometric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a biometric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in
proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of
candidate identity templates from the database for explicit comparison. In this paper we propose a coarse-
to-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to
reduce the response time. The proposed approach is tested on the database collected at our institute.
Proposed approach is of significance since hand geometry and palm print features can be extracted from
the palm region of the hand. Also performance of identification system is enhanced by reducing the
response time without compromising the identification accuracy.
KEYWORDS
Palm print, Hand geometry, Identification, Binning, Indexing.
1.INTRODUCTION
With the increasing emphasis on security, developing Computer aided person identification
system is becoming increasingly important in our present day information society. Tradition
identification approaches such as what a person knows or what a person has are not sufficiently
reliable to satisfy the requirements of identification systems which may be fake or cracked. To
meet the requirements of identification systems, Biometric systems has emerged as new type
solutions to our society’s ever increasing demand of improved security requirements.
Applications such as passenger control in airports, access control in restricted areas, border
control, database access and financial services are some of the examples where the biometric
technology has been applied. The techniques for identifying an individual based on his/her
physiological or behavioural characteristics are called as Biometrics [1-3]. Common
physiological biometrics includes finger print, face, iris, ear, hand vein, hand geometry and palm
print traits. Behavioural biometrics includes signature, gait, voice and hand writing traits.
Biometric system consists of two subsystems, one for enrolment and second one for recognition.
In the enrolment stage, biometric data are acquired from the individuals, feature sets are extracted
2. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
14
from the acquired data, and one or multiple templates per individual are computed and stored in
the database. In the recognition stage, biometric data of an individual is captured, feature set is
computed (query template) and then it is compared with the templates in the database created
during enrolment. Biometric systems can operate in two modes, Verification and Identification.
Verification refers to confirming or denying a person’s claimed identity. In this mode the system
performs one to one comparisons of the query template with the individual’s own biometric
templates stored in the database. Identification refers to establishing a person’s identity. In this
mode, query template is compared with the templates of all persons enrolled in the database to
establish an individual's identity. With the increase in the size of the biometric database these
comparisons not only increases the time required to declare an individual’s identity but also the
identification error rate. Hence there is a need for new approaches for retrieving relevant
candidate identities from the database against which comparison are performed to identify an
individual. The retrieval of a small number of identities from database based on probe template is
known as database filtering which can be achieved by classification or indexing schemes.
Therefore new representation schemes that allow for faster search and shorter retrieval time are
needed.
Biometric systems based on single biometric trait are referred to as unimodal systems. A single
trait of an individual can sometimes be insufficient for identification due to few reasons like noise
in acquired image, non universality and spoofing. For these reasons multimodal biometric
systems i.e., systems that integrate two or more biometric traits are being developed to overcome
the disadvantages of unimodal systems particularly to increase the recognition accuracy and to
decrease the possibility of circumventing the system. In contrast to biometric traits like face and
iris hand related traits such as palm print, hand geometry and inner knuckle print are attracting
considerable attention in biometric research community since hand image acquisition can be done
with reduced complexity set up. In this type of set up user need not hold any pegs or touch any
peripheral for their hand images to be acquired. Such a set up is believed to satisfy public’s
demand for non invasive and hygienic biometric technology. Since palm print and hand geometry
features can be extracted from a single image, in our work we have selected these two modalities
for developing a novel biometric identification system for performance enhancement.
Rest of the paper is organized as follows. Section 2 presents a brief review of research in palm
print and hand geometry. Section 3 describes the proposed approach. Performance of the
proposed scheme is presented in section 4 followed by the last section that concludes the
presented work.
2.LITERATURE SURVEY
Work that appears in literature with respect to palm print feature extraction are mainly lines based
[4-6], sub space [7-11 ] and transform based approaches [12-15]. Line based approaches use
either existing edge detection methods or develop edge detectors to extract palm lines which are
matched directly or represented in other formats for matching two palm print images to make
final decision. Sub space approaches use methods like principal component analysis, independent
component analysis and liner discriminative analysis in which subspace coefficients are regarded
as features. Distance based measures are used for comparing the features. Transform based
approaches utilizes statistical methods to transform images into other domain. Gabor wavelets
and Fourier transforms are the most commonly used approaches. Other approaches[16-18]
combine many image processing techniques and use some standard classifiers to make final
decision of recognition. Hand geometry features generally used are finger length and widths,
palm length and width, area/size of palm [19-22].
3. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
15
Existing research on palm print and hand geometry is focused mainly on feature extraction and
not on hand geometry feature selection. The main aim of this work is to identify a hand geometry
feature that reduces search space by retrieving relevant candidate identities from the database
against which comparisons are performed to identify an individual. Biometrics systems that use
combination of hand traits have been proposed by various researchers. But these systems mainly
address the accuracy issues of biometric systems neglecting the scalability and identification time
issues of large databases. Hand geometry features are not descriptive enough for identification of
individuals in large database. Whereas palm print features are reliable since they can serve as
unique biometric identifier as reported in biometric literature. Based on the above mentioned fact
in the proposed approach the gallery set database is partitioned into several bins using hand
geometry feature of enrolees. During identification, probe template hand shape feature will be
used to identify to which bin the probe template will match. Final identification is then conducted
in the reduced search space using palm print modality that has a higher matching accuracy. Very
few researches have studied the use of indexing unimodal biometric database to reduce the search
space so that the matching phase deals with only a subset of entire database. Hence reduction of
search space in biometric databases still remains as a challenging problem.
3.PROPOSED HIERARCHICAL APPROACH
Figure 1 illustrates the hierarchical frame work of the proposed system. The proposed system
works in two stages. First stage consists of image acquisition, pre processing and features
extraction components. In the pre-processing stage the alignment and orientation of the hand
images are corrected and key points on palm region are found for use in the successive stages. In
feature extraction stage the most discriminating features from the hand are extracted. Second
stage consists of proposed hierarchical approach and identification strategy. The details of each
stage are discussed in the subsequent sections.
3.1.Image Acquisition and Pre processing
The image acquisition module uses digital camera, tripod for image stability and well defined
background. This set up provides a simple, noncontact, comfortable and user friendly acquisition.
Our data collection process spanned over one month and 448 persons volunteered for the
database.
4. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
16
Figure 1 Proposed hierarchical framework
The participants were mainly students from our institute and were in the age group of 20 to 24
years. Four hand images of each individual were captured in two sessions resulting in 8 images
per individual leading to a total of 3584 images in the database. Complete image database is
divided into two mutually exclusive gallery (training) and probe (test) sets. The gallery set
consists of images with known identities (ID’s). However, probe set consists of images whose
identity is to be known. Let DBG = {IG1, IG2,……. IGN } be the gallery set database consisting of
N hand images and DBP = {QP1, QP2,……. QPM }be the probe set database consisting of M hand
images.
Pre processing process involves the following steps.
a) Captured colour image is converted to gray level image since gray level image is
adequate for extracting the features.
b) A fixed threshold is applied to convert the gray image into a binary image.
c) Hand contour is obtained and three key points are identified as per the algorithm
proposed in [23] with certain modifications.
Identified key points P1,P2 and P3 are marked as filled circles as shown in figure 2
3.2.Feature Extraction
3.2.1 Hand Geometry feature extraction
In view of reducing the dimension of feature vector key points P1, P2 and P3 identified during
pre processing stage are used for feature extraction. The angle between the line joining the points
P1,P2 and P2,P3 is calculated. The angle value is stored as hand geometry feature vector of one
dimension. In order to check the suitability of this feature two hand images of the same person
taken in two sessions were selected. The two angle values obtained are 151 and 156 degrees
respectively. It can be observed from figure 3 and 4 that even though spacing between fingers are
5. International Journal on Computational Science & Applications (I
different there is not much difference in the angle values computed for first session and second
session images.
3.2.2 Palm Print feature extraction
For cropping palm print ROI following steps are used.
a. Two key points are selected from the key points identified
First point is the valley point between little finger and ring finger, second point is the
valley point between middle finger and index finger. These points are considered as
anchor points.
Figure 2 Key Points identified
Figure 3 Hand image of first session
b. ROI of palm print is the rectangle region selected using the anchor points. The width of
ROI is considered as the distance
region is selected 20 pixels just below the first anchor point.
The ROI part containing the palm print is cropped out of the main image and then resized to a
size of 125 X 125 pixels. The resized
A sample of the input image along with anchor points, palm print ROI region and extracted ROI
of palm print is shown in Fig. 5 and
Local binary pattern (LBP) has been widely used in
texture information from biometric images [24
pixel in the image by thresholding it against the eight neighbo
International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
much difference in the angle values computed for first session and second
.2.2 Palm Print feature extraction
For cropping palm print ROI following steps are used.
Two key points are selected from the key points identified during preprocessing stage.
First point is the valley point between little finger and ring finger, second point is the
valley point between middle finger and index finger. These points are considered as
Figure 2 Key Points identified betwe en finger valley positio n
Figure 3 Hand image of first session Figure 4 Hand image of second session
ROI of palm print is the rectangle region selected using the anchor points. The width of
ROI is considered as the distance between anchor points and left top corner of rectangular
region is selected 20 pixels just below the first anchor point.
The ROI part containing the palm print is cropped out of the main image and then resized to a
size of 125 X 125 pixels. The resized palm print ROI image undergoes no further pre processing.
A sample of the input image along with anchor points, palm print ROI region and extracted ROI
and 6 respectively.
Local binary pattern (LBP) has been widely used in biometric recognition systems to extract
mation from biometric images [24]. The LBP operator assigns a binary label to every
by thresholding it against the eight neighbourhood pixels. If the pixel's value is
JCSA) Vol.5, No.5,October 2015
17
much difference in the angle values computed for first session and second
during preprocessing stage.
First point is the valley point between little finger and ring finger, second point is the
valley point between middle finger and index finger. These points are considered as
n
Figure 4 Hand image of second session
ROI of palm print is the rectangle region selected using the anchor points. The width of
between anchor points and left top corner of rectangular
The ROI part containing the palm print is cropped out of the main image and then resized to a
palm print ROI image undergoes no further pre processing.
A sample of the input image along with anchor points, palm print ROI region and extracted ROI
biometric recognition systems to extract
The LBP operator assigns a binary label to every
the pixel's value is
6. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
18
greater than the neighbour a value 1 is assigned, otherwise 0 is assigned. A binary label is called
uniform if it consists of at most two bit-wise transitions from 0 to 1 or vice-versa. For example
11101111 and 11111101 are uniform binary labels whereas 10101111 and 01011011 are non-
uniform. There are 58 labels of uniform patterns and the rest 198 labels are of non-uniform. A
label is given to each of the uniform patterns, and all other ‘‘non-uniform” patterns are assigned
to a single label resulting in 59 labels. After all the labels have been determined, a histogram Hl of
the labels is constructed as
ܪ = ∑{ܮሺ݅, ݆ሻ = ݈} , ݈ = 0, … , n − 1 ሺ1ሻ
Where n is the number of different labels produced by the LBP operator, while i and j refer to the
pixel location. LBP histograms calculated from palm print ROI are stored as feature vectors of 59
dimensions.
Figure 5 Identified palm ROI region Figure 6 Extracted ROI of palm
3.3. Hierarchical approach
In our work we use angle feature as coarse level feature to partition the database into several bins.
Although partitioning reduces the search space, comparing the query template sequentially with
all the templates of a partition is still expensive in terms of search time. Traditional database are
indexed numerically or alphabetically to reduce the retrieval time of records from database. In
biometric database, biometric templates do not posses any natural or alphabetical order. Hence,
there is a need of indexing technique which stores the templates in some predefined manner in
such a way that an efficient retrieval strategy can be used. In our proposed approach we make use
of a tree structure to organize the data. To facilitate retrieval strategy, we have used Kd tree [25]
to implement the indexing operation on partitioned database.
kd-tree [26] is a binary tree that represents hierarchical sub division of space using splitting
planes. It is a space partitioning data structure for organizing points in a k dimensional space and
useful for searches involving multi dimensional search key. Number of nodes in a kd-tree is equal
to number of templates in the gallery set and k denotes the dimensionality of the template. Each
node in a kd-tree consists of information field, discriminator field, value field and two pointer
fields. Information field contains descriptive information about the node, value field contains
feature vector value, discriminator field will take a value from 1 to k (all nodes at any given level
of a tree will have same discriminator value) and pointer fields will have pointer value of left &
right sub tree of the node. At the root data points are split into two halves by a partition hyper
plane and each half is assigned to one child node. Each of the two halves is recursively split in the
same way to create a balanced binary tree. The proposed system reduces the search time since kd-
tree supports range search with good pruning.
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3.3.1.Binning and Indexing
Let B = { ( b1L , b1U ) , ( b2L , b2U ) ,..... ( bmL , bmU ) }, be the bin class representative set. Where (
biL , biU ) corresponds to interval values assigned to ith
bin such that b1L< b1U < b2L < b2U < ….. bmL
< bmU and m represents number of bins. b1L is the least value that angle feature can take and bmU
is the maximum value.
The steps carried out in this phase are as follows.
a) An Hand image from gallery set is pre processed to locate key points.
b) Angle feature and palm print feature of hand image are computed as explained in feature
extraction section to get feature vectors (templates). ID’s of each template is also
maintained.
c) Angle feature vector value is compared with bin interval values to find to which bin it
maps to. Corresponding palm print feature vector is stored into the mapped bin.
d) Step (a) to (c) is repeated for all images in gallery set.
e) Form kd-trees for each bin using the palm print features stored in respective bins. ID's of
templates mapped are also maintained.
3.3.2.Identification Strategy
Algorithm for retrieval of top best match for a query image is given below.
Algorithm Identification:
Step 1: For a query image q perform pre processing and feature extraction procedure to get the
query angle feature vector and palm print feature vector.
Step 2: Compare query angle value with bin interval values to find to which bin it maps.
Step 3: A ‘k’ nearest neighbour search on Kd-tree of selected bins is invoked using the palm print
feature vector. These nearest neighbours form the candidate set C that contains identities of
templates satisfying ∀ i ∈ C
∥ ݍ − ݅ ∥ ≤ ∥ – ݍ ∥ , ∀ p ∈ ሺDBୋ − Cሻ ሺ2ሻ
Where ു . ു is distance measure.
Step 4: Accumulate only those identities in C whose distance is less than threshold T.
Step 5: Declare the top best match as the identity of q.
4.EXPERIMENTAL RESULTS
To evaluate the performance of proposed approach four images of each individual from first
session are considered for gallery set resulting in 1792 images in gallery set. Any one image out
of four images from second session is randomly selected for probe set resulting 448 images in
probe set. In our work we use Euclidean distance between a probe template and a gallery set
template for describing the similarity between them. A threshold is used to regulate the system
decision. If the Euclidean distance between the pair of templates computed is less than the
threshold then the pair of templates is considered to belong to the same person. Consequently if
the distance is greater than the threshold then the pair of templates is considered to belong to
different person. Performance of the proposed scheme is evaluated using three measures namely
Bin penetration rate, Correct Index Power (CIP) and Bin miss rate. A query image is said to be
correctly identified, if one of the retrieved identities corresponds to the correct identity.
The Bin penetration rate Pr is defined by
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20
ܲ =
ܰௌ × ܣ் + ܰ
ܰ
ሺ3 ሻ
where ܰௌ is the number of bins to be searched, ܣ் is the average templates per bin, ܰ is
number of bins and ܰ is the size of the database.
CIP is defined by
ܲܫܥ =
ܥ
ܲ
ሺ4ሻ
Where C is the number of query images correctly identified and P is the number of query images
in the probe set.
Bin miss rate is defined by
ܤ = 1 −
ܤ
ܮ
ሺ5ሻ
where B is the number of times a query's corresponding identity is found in the selected bin and L
is the total number of queries.
Hand geometry features of gallery set images are used to find the ID's that are mapped into bins.
In our work we have selected the number of bins as six and this value is based on initial
experiments carried out to find the minimum and maximum angle value of gallery set images.
The efficiency of the binning scheme is measured in terms of bin miss rate and bin penetration
rate. Figure 7 shows the effect of varying number of bins on bin penetration rate. Figure 8 shows
the relationship between bin miss rate and number of bins selected. From figure 8 we can infer
that bin miss rate is zero if three closet bins are selected.
Figure 9 shows the relationship between bin miss rate and bin penetration rate with respect to
number of bins selected. The number of bins selected is considered as optimal value where the
two curves intersect. Since the two curves intersect for a x-axis value between 2 and 3, number
of bins selected for identifying probe is considered as 3. This analysis is done assuming a uniform
distribution of bin density for all the bins. The actual bin density are 260, 290, 330,310,352,250.
If probe template maps to first bin or second bin, then bin 1 ,2 and 3 are selected. If mapped to
third bin then bin 2,3 and 4 are selected and for mapping to fourth and fifth bin then bin 4,5 and 6
are selected. Our analysis of penetration rate hold true since when three consecutive bins are
selected average percentage of gallery set templates considered is almost equal 52%. This fact is
illustrated in table 1. In conventional approach a query template is compared with all 1792
templates (100%) stored in the database. With the proposed approach percentage of database
considered for second stage is 52%, hence database filtered at first stage by binning approach is
48%.
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Figure 7 Relationship between Bin penetration rate and Number of bins
Figure 8 Relationship between Bin miss rate and Number of bins
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Figure 9 Relationship between Bin miss rate/ Bin penetration rate and Number of bins
In order to fix the threshold value verification experiments were carried out for palm print
modality using LBP features. Personal verification is one to one matching. Verification refers to
confirming or denying a person’s claimed identity. Two types of error rates namely false rejection
rate (FRR) and false acceptance rate (FAR) are defined in biometric literature with respect to
verification method. FRR is the percentage of authorized users that the biometric system fails to
accept and FAR is the percentage of unauthorized users that the biometric system fails to reject.
Equal error rate (ERR) is the optimal rate when FAR equals FRR. The threshold value obtained
when FAR equals FRR is 0.0005. This threshold value is used in steps 4 of identification
algorithm.
Nearest neighbour(NN) search on the selected three kd-trees is invoked for each query palm print
template selected from probe set as mentioned in identification algorithm to get candidate lists.
Since k-NN search uses Euclidean distance, from the three kd-trees all identities with distance
less than threshold T are considered for candidate list. Also k value is varied from 10% of entries
in kd-tree to 100%.
Table 1 Penetration rate analysis with proposed approach and conventional approach
With proposed approach Conventional
Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6
Complete gallery
set
Number of
templates in
260 290 330 310 352 250 1792
Number of
templates
considered
when probe
template is
mapped to
880 880 930 992 912 912 1792
Penetration
rate
49% 49% 52% 55% 51% 51% 100%
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Figure 10 Plot of CIP for varying percentage of Bin search
The CIP curve obtained for varying k value is shown in figure 10. From this plot we can infer that
corresponding identities are found by searching 30% of nearest neighbours in kd-tree. It can be
noted that 94% identification accuracy is achieved and this is the same accuracy that is achieved
by sequential approach.
Biometric system proposed in [27-30] makes use of more than one modality to enhance the
system performance. These approaches establish the identity associated with query image by
considering the complete gallery set and focuses mainly on enhancing the accuracy. However
proposed work focuses on reducing response time by reducing search space while maintaining
accuracy. Also majority of research work uses palm print images acquired using pegs or holdings
where as in our proposed work contactless image acquisition set up is used.In conventional
approach, test template corresponding to a test image selected from probe set is compared with
templates of all images from the gallery set. This exhaustive comparison can make the response
time of system very poor due to more number of comparisons.The computational time analysis of
proposed approach and conventional approach methods for searching one query template of probe
set against gallery set templates is tabulated in Table 2. From Table 2 it is clear that the proposed
approach is more effective than the conventional approach. Time for preprocessing and ROI
extraction of one hand image is 2 sec and time for feature extraction is 0.125 sec.
Table 2 Time analysis of proposed approach and conventional approach
With proposed approach Conventional
% of bin searched 10% 20% 30% 40% 100%
Time for estblaishing the
identity using probe
feature vector
0.005 sec 0.007sec 0.01 sec 0.015 sec 0.96 sec
Time for overall process
for one probe image
2.13 sec 2.132 sec 2.135 sec 2.14 sec 3.085 sec
Correct index power in % 88 92 94 94 94
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CONCLUSIONS
An novel binning and indexing approach to retrieve identity of a query image based on hand
geometry and palm print features is proposed in this paper. For a query feature template using
hand geometry feature three kd trees are selected in the first stage by binning approach. Since kd-
tree is used to index the partitioned database a nearest neighbor search is invoked on the selected
kd-trees to retrieve the top matches. These top matches are subsequently used for person
identification. The obtained experimental results are encouraging and we claim that proposed
binning and indexing approach gives the same identification accuracy as obtained by
conventional database search with 0.95seconds reduction in search time for 48% reduction in
search space.
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