Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
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
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
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
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
The biometric technology is used to identify
individuals effectively compared to existing traditional
methods. In this paper we propose Bimodal Biometric System
using Multiple Transformation features of Fingerprint and
Iris (BBMFI). The iris image is preprocessed to generate iris
template. The two level Discrete Wavelet Transformation
(DWT) is applied on iris template and Discrete Cosine
Transformation (DCT) is performed on second level low
frequency band to generate DCT coefficients which results in
features of iris. The fingerprint is preprocessed to obtain
Region of Interest (ROI) and segmented into four cells. Then
the DWT is applied on each cell to derive approximation band
and detailed bands. The Fast Fourier Transformation (FFT)
is applied on approximation band to compute absolute values
that results in features of fingerprint. The iris features and
fingerprint features are fused by concatenation to obtain final
set of features. The final feature vector of test and database
are compared using Euclidean distance matching. It is observed
that the values of Total Success Rate (TSR), False Rejection
Rate (FRR) and False Acceptance Rate (FAR) are improved in
the proposed system compared to existing algorithm.
A Novel Biometric Approach for Authentication In Pervasive Computing Environm...aciijournal
The paradigm of embedding computing devices in our
surrounding environment has gained more interest
in recent days. Along with contemporary technology
comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of
compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed
for identification should be robust and dynamic. In
this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and retinal scanning algorithms have been discussed whi
ch promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
Adversarial Multi Scale Features Learning for Person Re Identificationijtsrd
Person re identification Re ID is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Pried can be considered as a problem of image retrieval. Existing person re identification methods depend mostly on single scale appearance information. In this work, to address issues, we demonstrate the benefits of a deep model with Multi scale Feature Representation Learning MFRL using Convolutional Neural Networks CNN and Random Batch Feature Mask RBFM is proposed for pre id in this study. The RBFM is enlightened by the drop block and Batch Drop Block BDB dropout based approaches. However, great challenges are being faced in the pre id task. First, in different scenarios, appearance of the same pedestrian changes dramatically by reason of the body misalignment frequently, various background clutters, large variations of camera views and occlusion. Second, in a public space, different pedestrians wear the same or similar clothes. Therefore, the distinctions between different pedestrian images are subtle. These make the topic of pre id a huge challenge. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state of the art on the popular benchmark datasets including Market 1501, duke mtmc re id and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods. Mrs. D. Radhika | D. Harini | N. Kirujha | Dr. M. Duraipandiyan | M. Kavya "Adversarial Multi-Scale Features Learning for Person Re-Identification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42562.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/42562/adversarial-multiscale-features-learning-for-person-reidentification/mrs-d-radhika
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
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.
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.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Integrating Fusion levels for Biometric Authentication SystemIOSRJECE
— Recently a lot of works are presented in the literature for the multimodal biometric authentication. And such biometric systems have been widely accepted with increasing accuracy rates and population coverage, reducing the vulnerability to spoofing. This paper descripts about the proposed multimodal biometric system that combines the feature extraction level and the score level fusion of iris and face unimodal biometric systems in order to take advantage of both fusion techniques. The experimental results shows the performance of the multimodal and multilevel fusion techniques which are analysed using TRR and TAR to study the recognition behaviour of the approach system. From the ROC Curve plotted, the performance of the proposed system is better compared to the individual fusion techniques.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
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.
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.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
The biometric technology is used to identify
individuals effectively compared to existing traditional
methods. In this paper we propose Bimodal Biometric System
using Multiple Transformation features of Fingerprint and
Iris (BBMFI). The iris image is preprocessed to generate iris
template. The two level Discrete Wavelet Transformation
(DWT) is applied on iris template and Discrete Cosine
Transformation (DCT) is performed on second level low
frequency band to generate DCT coefficients which results in
features of iris. The fingerprint is preprocessed to obtain
Region of Interest (ROI) and segmented into four cells. Then
the DWT is applied on each cell to derive approximation band
and detailed bands. The Fast Fourier Transformation (FFT)
is applied on approximation band to compute absolute values
that results in features of fingerprint. The iris features and
fingerprint features are fused by concatenation to obtain final
set of features. The final feature vector of test and database
are compared using Euclidean distance matching. It is observed
that the values of Total Success Rate (TSR), False Rejection
Rate (FRR) and False Acceptance Rate (FAR) are improved in
the proposed system compared to existing algorithm.
A Novel Biometric Approach for Authentication In Pervasive Computing Environm...aciijournal
The paradigm of embedding computing devices in our
surrounding environment has gained more interest
in recent days. Along with contemporary technology
comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of
compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed
for identification should be robust and dynamic. In
this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and retinal scanning algorithms have been discussed whi
ch promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
Adversarial Multi Scale Features Learning for Person Re Identificationijtsrd
Person re identification Re ID is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Pried can be considered as a problem of image retrieval. Existing person re identification methods depend mostly on single scale appearance information. In this work, to address issues, we demonstrate the benefits of a deep model with Multi scale Feature Representation Learning MFRL using Convolutional Neural Networks CNN and Random Batch Feature Mask RBFM is proposed for pre id in this study. The RBFM is enlightened by the drop block and Batch Drop Block BDB dropout based approaches. However, great challenges are being faced in the pre id task. First, in different scenarios, appearance of the same pedestrian changes dramatically by reason of the body misalignment frequently, various background clutters, large variations of camera views and occlusion. Second, in a public space, different pedestrians wear the same or similar clothes. Therefore, the distinctions between different pedestrian images are subtle. These make the topic of pre id a huge challenge. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state of the art on the popular benchmark datasets including Market 1501, duke mtmc re id and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods. Mrs. D. Radhika | D. Harini | N. Kirujha | Dr. M. Duraipandiyan | M. Kavya "Adversarial Multi-Scale Features Learning for Person Re-Identification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42562.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/42562/adversarial-multiscale-features-learning-for-person-reidentification/mrs-d-radhika
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
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.
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.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm IJECEIAES
The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Integrating Fusion levels for Biometric Authentication SystemIOSRJECE
— Recently a lot of works are presented in the literature for the multimodal biometric authentication. And such biometric systems have been widely accepted with increasing accuracy rates and population coverage, reducing the vulnerability to spoofing. This paper descripts about the proposed multimodal biometric system that combines the feature extraction level and the score level fusion of iris and face unimodal biometric systems in order to take advantage of both fusion techniques. The experimental results shows the performance of the multimodal and multilevel fusion techniques which are analysed using TRR and TAR to study the recognition behaviour of the approach system. From the ROC Curve plotted, the performance of the proposed system is better compared to the individual fusion techniques.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
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.
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.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Re...ijscmcj
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality.
Gated recurrent unit decision model for device argumentation in ambient assis...IJECEIAES
The increasing elderly population worldwide is facing a variety of social, phys- ical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people pre- fer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among de- vices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
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.
Biometrics Authentication of Fingerprint with Using Fingerprint Reader and Mi...TELKOMNIKA JOURNAL
The idea of security is as old as humanity itself. Between oldest methods of security were
included simple mechanical locks whose authentication element was the key. At first, a universal–simple
type, later unique for each lock. A long time had mechanical locks been the sole option for protection
against unauthorized access. The boom of biometrics has come in the 20th century, and especially in
recent years, biometrics is much expanded in the various areas of our life. Opposite of traditional security
methods such as passwords, access cards, and hardware keys, it offers many benefits. The main benefits
are the uniqueness and the impossibility of their loss. The main benefits are the uniqueness and the
impossibility of their loss. Therefore we focussed in this paper on the the design of low cost biometric
fingerprint system and subsequent implementation of this system in praxtise. Our main goal was to create
a system that is capable of recognizing fingerprints from a user and then processing them. The main part
of this system is the microcontroller Arduino Yun with an external interface to the scan of the fingerprint
with a name Adafruit R305 (special reader). This microcontroller communicates with the external database,
which ensures the exchange of data between Arduino Yun and user application. This application was
created for (currently) most widespread mobile operating system-Android.
Using Brain Waves as New Biometric Feature for Authenticating a Computer User...CSCJournals
In this paper we propose an Electroencephalogram based Brain Computer Interface as a new modality for Person Authentication and develop a screen lock application that will lock and unlock the computer screen at the users will. The brain waves of the person, recorded in real time are used as password to unlock the screen. Data fusion from 14 sensors of the Emotiv headset is done to enhance the signal features. The power spectral density of the intermingle signals is computed. The channel spectral power in the frequency band of alpha, beta and gamma is used in the classification task. A two stage checking is done to authenticate the user. A proximity value of 0.78 and above is considered a good match. The percentage of accuracy in classification is found to be good. The essence of this work is that the authentication is done in real time based on the meditation task and no external stimulus is used.
New hybrid ensemble method for anomaly detection in data science IJECEIAES
Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier. Thus, a development of edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection includes detection of edge with lower rorrate ,whichmeans that the detection should accurately catch as many edges.
Ataxic person prediction using feature optimized based on machine learning modelIJECEIAES
Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a
software framework used with Apache Hadoop, which has become the de facto standard platform for
processing and storing large amounts of data in a distributed computing environment. The research
presented here focuses on the variations observed among the results of an efficient iterative transitive
closure algorithm when run against different distributed environments. The results from these comparisons
were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The
experiment results highlighted the inconsistencies that can occur when using the same codebase with
different implementations of Map Reduce.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
The Benefits and Techniques of Trenchless Pipe Repair.pdf
Feature Extraction Methods for IRIS Recognition System: A Survey
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
DOI: 10.5121/ijcsit.2022.14107 99
FEATURE EXTRACTION METHODS FOR IRIS
RECOGNITION SYSTEM: A SURVEY
Tara Othman Qadir1
, Nik Shahidah Afifi Md Taujuddin2
, Sundas Naqeeb Khan3
1, 2
Faculty of Electrical and Electronic Engineering,
Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Johor, Malaysia
3
Faculty of Computer Science and Information Technology,
Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
ABSTRACT
Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech
checker, and so on. A number of methods for effective iris detection have indeed been suggested and
researched. A general overview of current and state-of-the-art approaches to iris recognition is presented
in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and
methodologies for the extraction of features are also discussed.
KEYWORDS
Bio-metric traits, iris patterns, feature extraction, SVM, wavelet transform, iris security.
1. INTRODUCTION
The most significant security issue today is verification; if researchers can enhance this area, it
implies they are reducing security threats. Various secure techniques were used, including
security, but today a biometric technology known as iris recognition provides security in terms of
verification. Humans live in a safe environment owing to the unique iris pattern, but they also
have evil genius brains that can break the protection. As a result, academics are working to
develop more secure iris recognition technologies for a more safe society [1].
There are three focused categories of verification, such as starting from password, but this was a
very weak way to secure any system or object from hackers. The next method was card or token,
but that was also a very low-level security method. Anyone could present a card or token on their
own. The last step for security is biometric, and this method provides real security to
verification. According to the biometric method, no one can emulate or steal natural human
patterns [2 - 3].
Verification of a person based on physiological and behavioral aspects. Face, finger prints, palm
and hand geometry, DNA, retinal and iris trends are some of the most commonly observed
physical aspects in a person, while signatures, tone of voice, walking style, and keystrokes are
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
100
some of the most frequently observed behavioral aspects. From all of these above patterns, the
iris is the only method that is used for security verification [4].
Now a question arises about the word biometric. What is a biometric? It consists of two words.
Bios means life and metrikos means measurement, so when researchers use these two words
together, it becomes biometric. Therefore, a biometric system capable of identifying a person's
traits stands upon a feature vector [7].
Biometric systems consist of four major parts, such as the sensor unit, feature extraction element,
matching pattern, and decision response. Consequently, when a biometric system is applied to a
human trait, there are four basic conditions that a human must have, like entirety, uniqueness,
immovability, and collectability. There is a comparison between some biometric systems
according to their factors in terms of High (H), Medium (M) and Low (L) in table 1 [8].
TABLE 1. Comparison between biometric systems with their factors
In the modern age of secure applications, there are some traditional issues which have a great
impact on biometric systems, as described in Table 2 with their impact factor also in terms of
High, Medium, and Low [8].
TABLE 2. Biometric system traditional factors with their impact
As a result, iris recognition is a fully systematic biometric system in which issues are resolved
using various mathematical methods, and these methods are directly applied to individual eye
Biometric traits Entirety Uniqueness Immovability Collectability
Face recognition H L M H
Walking style M L L H
Keystroke dynamics L L L M
Odor H H H L
Ear M M H M
Hand geometry M M M H
Finger print M H H M
Retina H H M L
Palm print M H H M
Tone of voice M L L M
DNA H H H L
Signature L L L H
Iris H H H M
Biometric traits Performance Acceptability Circumvention
Face recognition L H H
Walking style L H M
Keystroke dynamics L M M
Odor L M L
Ear M H M
Hand geometry M M M
Finger print H M M
Retina H L L
Palm print H M M
Tone of voice L H H
DNA H L L
Signature L H H
Iris H L L
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
101
images that are considered distinctive [9 - 11]. There are many possible approaches which can be
used for iris recognition, but formally, researchers divided these approaches into three known
categories, such as supervised, unsupervised, and semi-supervised approaches. In supervised
approaches, trained data is available for testing by using different classifiers, while according to
unsupervised learning approaches, using unlabelled data, the working style of this approach is
slightly different from the supervised approach. If the data pool has a smaller amount of trained
data and a huge amount of untrained data, then researchers recommend the semi-supervised
approaches.
The first section contains an introduction to the study that is relevant to the research. The second
section is about the related research work, and the third portion describes the methodology of the
research. The results and discussion section is under the umbrella of the fourth portion of the
study, and at the end, the conclusion is included as a final discussion.
2. LITERATURE REVIEW
There are some main contributed articles that are considered related works. Due to certain
resolving issues, iris recognition systems are considered the main stream for security verification
of individuals. Nanik Suciati [12] presents an automatic recognition system for a person's
identification based on eye image. Canny Edge Detection (CED) with Hough Transform
techniques used for iris detection, followed by features selected by Wavelet Transform at the last
Support Vector Machine (SVM) classifier trained for feature representation, provides 93.5% of
the results. In [13], researchers worked on optimizing attribute mining according to the wavelet
task, while for the similarity method, they used multi-class SVM with an ant colony algorithm
and gave better outcomes in terms of performance.
Tejas's [14] research concept is based on energy compression, and three different Self Mutated
Hybrid Wavelet Transforms (SMHWT) methods are used to generate feature vectors.
Characteristics basic purpose of this research is to reduce vector size, with the help of partial
energy and the Genuine Acceptance Rate (GAR) metric. Cosine-Haar provides the best GAR
accuracy rate. Researchers [15] provide scattering and textural feature sets for the reduction of
dimensionality according to the Principle Component Analysis (PCA) method and the minimum
distance classifier algorithm, which are also used for matching and get a 99.2% accuracy
rate. Kiran [16] gives the idea of vigorous segmentation of detectable iris examples while
estimating the radius of the iris with a new deep sparse filtering algorithm for unsupervised
learning. The proposed method shows 85% accuracy in correct results on both the existing
dataset and the newly generated dataset VSSIRIS. Authors [17] give the idea of attribute mining
the name "vigorous keypoints method". In this method, they merge three detectors as regards
SIFT features for corresponding score points. The unions take care of the calculation of weights
with summation regulations and provide competitive performance as compared to baseline
methods.
Lydia Elizabeth [18] presents her work in 2014 on a grid-based algorithm for feature extraction
that combines Singular Value Detection (SVD) and Discrete Wavelet Transform (DWT).
Therefore, this hybrid process offers a powerful, protected, and imperceptible watermarking
method with a minimum fault acceptance rate in good behavior. Imen Tajouri [19] improves on
Rai's algorithm by combining HAAR wavelet, 2D Log Gabor, and a monogenic filter for feature
extraction. This shows the 94.45% empirical results of the proposed method as compared to the
Daubechies wavelet and Histogram of Oriented Gradient (HOG). A deep learning approach
named convolutional neural network is integrated with a fusion method for iris recognition [43].
The feed forward mechanism proposed along a clustering method k-mean for the iris feature
extraction. The approach reduces the calculated time and size of source link as well as improves
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
102
the iris recognition [44]. An intelligent method presented for iris feature extraction and matching
activity in which the two hybrid methods used for this activity. Besides this, machine learning
algorithm is also include in the research as apart of matching approach which gives more efficient
results [45].
To address the low false rejection issue in feature extraction, the proposed Combined Directional
Wavelet Filter Bank (CDWFB) [20] algorithm combines the Directional Wavelet Filter Bank
(DWFB) and the Rotated Directional Wavelet Filter Bank (RDWFB). This approach extracts the
texture of the iris in 12 directions and provides excellent results as compared to more exciting
approaches. Researchers proposed [21] a hybrid technique design based on sparse demonstration,
including three classifiers for classes’ short list and further work on classes after that work
combining these classifiers with genetic algorithms to provide the best results. Table 3 shows the
considered articles as reviewed for related work and explains the main contributions of the
researchers.
TABLE 3. Biography of under consideration articles
Author name Article name
Search
engine
Amol D. Rahulkar and
Raghunath S. Holambe
[14]
Partial iris feature extraction and recognition based on a new
combined directional and rotated directional wavelet filter
banks
Elsevier
Vijay Prakash Sharma,
et al [7]
Improved Iris Recognition System using Wavelet Transform
and Ant Colony Optimization
IEEE
Lydia Elizabeth. B, et
al [12]
A grid based iris biometric watermarking using wavelet
transform
IEEE
Shervin Minaee, et al
[9]
Iris recognition using scattering transform and textural features IEEE
Kiran B. Raja, et al
[10]
Smartphone based visible iris recognition using deep sparse
filtering
Elsevier
Nanik Suciati, et al [6]
Feature Extraction Using Statistical Moments of Wavelet
Transform for Iris Recognition
IEEE
Tejas H. Jadhav and
Jaya H. Dewan [8]
Iris Recognition using Self Mutated Hybrid Wavelet Transform
using Cosine, Haar, Hartley and Slant Transforms with Partial
Energies of Transformed Iris Images
IJCA
Yuniol Alvarez-
Betancourt and Miguel
Garcia-Silvente [11]
A keypoints-based feature extraction method for iris
recognition under variable image quality conditions
Elsevier
Imen Tajouri, et al [13]
An Efficient Iris Texture Analysis Based On HAAR Wavelet
2D Log Gabor and Monogenic Filter
IEEE
Ashok K Bhateja, et al
[15]
Iris recognition based on sparse representation and k-nearest
subspace with genetic algorithm
Elsevier
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
103
3. METHODOLOGY
Iris recognition is performed by the different biometric systems. Due to certain specifications, the
evaluation process of iris recognition systems is divided into four major modules, which are
mentioned in Figure 1.
The very first step of iris recognition is acquiring the iris images from different types of objects
through electronic devices like cameras or sensors etc. Each image has elucidation, location area
among corporeal incarcerate structure, and other factors such as occlusion, illumination, and pixel
extent play an important role in image eminence [22].
The second step is early stage processing, in which we check the iris liveness and edge, pupil,
eyelid, normalization, subtraction of iris etc. Through iris liveness recognition, the security
system can check if the focal object is alive because there is an option in which biometric aspects
are employed illegitimately. Localization of the iris and pupil is another important preprocessing
step that was developed by Zhaofeng [5 - 6].
As a result, the parabolic arcs perform conformant of the eyelids and then plot this extorted iris
area according to the normalization. All forces composition [23] comes from the commencement
of the summation of points which examine the iris and pupil centre within the radius. Some
functions attained iris boundaries through applied form in [24]. The basic law of iris localization
is based on incline strength along consistency divergence [25].
Classification is performed through extracted aspects of iris images in the third step, where some
aspects have important variants such as 90° axis, range and dimensions of pupil, strength,
direction according to ellipsoid shape, and all the features snatched from the iris images which
are useful for security verification are organized in this step. The last step used processed iris
images along with stored images for the matching process [26]. Due to inter-class and intra-class
variables, classification issues can be resolved. Table 4 describes some important methods of iris
recognition according to their influence on results in the form of performance, where Equal Error
Rate (EER), False Rejection Rate (FRR) and False Acceptance Rate (FAR) are used as
performance measures.
Image acquisition
Matching/Recognition
Feature extraction
Preprocessing
Image adequate
through electronic
devices or from
stored database
This early stage
required methods/
techniques for
cleanliness of
noisy data
Unique units of
images fetched
by different
methods
Decision regarding
acceptance or
rejection perform in
this stage
FIGURE 1. Iris recognition system
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
104
TABLE 4. Iris patterns based methods with their performance and average time
4. RESULTS AND DISCUSSION
Table 5 shows the performance of the under consideration articles by SVM, PCA, different
algorithms and classifiers with their accuracy. Normally, SVM used with the combination of
some type of filters and statistical methods such as SVM with wavelet transform and colony
Methods Reference
Stored patterns
in DB
Performance
Average time
taken (seconds)
Phase based
method
Daugman
[27,28]
4258 images EER: 0.08% 0.71, 0.68
Martin Roche
[29]
300 images FRR: 8% 0.89
Masek [30] 624 images
FAR: 0.005% and
FRR: 0.238%
0.92
Xiaomei Liu
[31]
12000 images
(ICE)
Recognition rate:
96.61%
0.78
Karen
Hollingsworth
[32]
(i) 1226 images
from 24 subjects
(ICE)
(ii) 1061 videos
from 296 eyes
(iii) ICE database
(iv) 1263 images
from 18 subjects
(ICE)
(i)HD=7.48
(ii)EER=3.88x10-3,
FRR =7.61x10-6,
FAR=0.001
(iii) HD=0.15
(iv)FRR=0.271, FAR
= 0.001, EER=0.068
for large pupil subset
Null
Texture analysis
based method
Wildes [33, 34,
35]
60 images EER: 1.76% 0.62, 0.69, 0.78
Emine Krichen
[36]
700 images
Improvement in
FAR: 2% and FRR:
11.5%
0.88
Zero crossing
representation
method
Boles [37] Real images EER: 8.13% 0.69
Intensity
variations based
method
Li Ma [26, 38]
2245 images
(CASIA)
Correct Recognition
Rate: 94.33%.
0.77, Null
Jong Gook Ko
[39]
(i) 820 images
from 82
individuals
(ii) 756 images
(CASIA)
Recognition rate:
98.21%
0.66
N. Tajbakhsh
[40]
1877 images
(UBIRIS)
ERR: 0.66%, FRR:
4.10% and FAR:
0.01%
0.71
Independent
Component
Analysis (ICA)
based method
Ya Ping Haung
[41]
Real images
Blurred iris: 81.3%,
Variant illumination:
93.8% and Noise
interference: 62.5%
0.65
Continuous
Dynamic
Programming
based method
Radhika [42]
(i)1205 images
(UBIRIS)
(ii)1200 images
(CASIAv2)
Acceptance Rate:
98%
Rejection Rate 97%
Null
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
105
gives 93.5% and 98% results respectively. On the other hand, PCA was used with distance
classifiers and provided better results, like 99.2% accuracy. The Deep Sparse Algorithm is a
filtering algorithm used for the VSSIRIS dataset and has shown 85% accuracy in empirical
tests. The CED algorithm is based on grid watermarking. This is used for the global iris
recognition dataset and minimizes the fault acceptance and error rate by approximately 77%. The
Genetic Algorithm (GA) is associated with three classifiers and helps to reduce the execution
time. There are many algorithms used for feature extraction, like enhancement in Rai’s algorithm
integrated with filters, while on the basis of keypoints extraction, there are marginal
improvements among three detectors such as Harris, Hessian, and Fast Laplace. For the reduction
of feature vector size, researchers used the self-mutated hybrid wavelet transform method and
adequate 14% improvement in the results.
Figure 2 shows the testing results of articles in diverse domains that contain the time and
frequency developed through the measurements of performance. Consequently, figure 3 describes
the measurement results in terms of performance according to their relevant datasets, and several
feature extraction methods and algorithms were applied to these datasets and improved the angle
of performance and accuracy. In figure 4, we select the articles that have diversity in methods
such as phase-based methods, texture analysis methods, zero crossing representation based
methods, intensity variations based methods, independent component analysis based methods,
and continuous dynamic programming based methods along with their performance according to
the FAR and FRR with recognition and error rate.
TABLE 5. Summary of performance under reviewed articles with different applied methods
Task Approach Dataset Result Primary objectives
Limitations/f
uture work
Iris
recogniti
on
system
[12]
SVM with
Wavelet
transform
CASIA
eye
image
93.5%
Detection of iris area with
suitable selected features and
then representation of these
features are the focus
objectives of this research.
Improvement
in results
regarding
accuracy and
execution time
Selection
of
optimize
d feature
[13]
SVM with ant
colony
CASIA
eye
image
99% for
FAR and
98% for
FFR
Selection and optimization
operations perform with the
help of multi class SVM and
ant colony process.
Reduction of
computational
time
Feature
vector
size
reduction
[14]
Self mutated
hybrid
wavelet
transforms
Palacky
Universit
y Iris DB
14%
improve
ment
The SMHWT reduce the
feature vector size and
Cosine-Haar used partial
energy with best
improvement in GAR
function.
Color spaces
use for better
performance
Dimensio
nality
reduction
of feature
vector
[15]
Principal
component
analysis
(PCA) with
minimum
distance
classifier
Iris DB
collected
by IIT
Delhi
99.2%
Reduce dimensions of two
proposed feature sets
according to PCA and
algorithm used for best
accuracy.
Proposed set
of features test
on other
datasets and
biometric
detection
issues
Robust
segmenta
tion for
iris
recogniti
deep sparse
filtering
algorithm
with
VSSIRIS
BIPLab
DB and
VSSIRIS
newly
created
85%
accuracy
A deep sparse filtering
method used for robust
segmentation of observable
range iris recognition
provides high outcomes on
Supervised
learning
improve the
accuracy
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
106
on [16] dataset DB newly created dataset.
Feature
extractio
n [17]
Keypoints-
based feature
extraction
method
CASIA-
IrisV4-
Interval,
MMU2,
and
UBIRIS
1DB
68%
Keypoints feature extraction
combine three detectors like
Harris, Hessian and Fast
Laplace as a SIFT features for
matching score level and
calculate the weights for
attain better performance.
Implementatio
n of real time
application
Feature
extractio
n [18]
Grid based
approach
used Canny
Edge
Detection
(CED)
algorithm
Global
iris
recognitio
n dataset
77%
Grid based watermarking
algorithm used with a hybrid
SVD and DWT for
minimizing fault acceptance
and error rate.
Watermarking
algorithm
accuracy
Feature
extractio
n [19]
Rai’s
algorithm for
attribute
extraction
CASIA
V1.0 and
CASIA
V3.0
94.45%
Enhance the Rai’s algorithm
with combination of
monogenic filter and 2D Log
Gabor filter
Gabor Ordinal
Measures
GOM) test for
feature
extraction
Feature
extractio
n [20]
Combined
Directional
Wavelet
Filter Bank
(CDWFB)
proposed
approach
UBIRIS
and
MMU1
DBs
99%
accuracy
for
UBIRIS
and 98%
accuracy
for
MMU1
CDWFB a new approach for
feature extraction consists of
two different filter banks and
provide better performance in
terms of accuracy.
Improve the
performance
on real time
applications
Reductio
n of time
[21]
Three
classifiers
with genetic
algorithm
CASIA
and IITD
DBs
99.43%
on
CASIA
and
99.20%
on IITD
DBs
accuracy
Three classifiers used with
genetic algorithm for sparse
representation for reducing
the time.
Improve FRR
and FAR with
accuracy on
real time
applications
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
107
FIGURE 2. Year wise distribution of articles
FIGURE 3. Accuracy performance measured by different datasets
FIGURE 4. Iris recognition methods with their approved performance
2010
2012
2014
2016
2018
[10]
[11]
[14]
[15]
[12]
[13]
[6]
[7]
[9]
[8]
Elsevier IEEE IJCA
Year
wise
distribution
Search engines
Frequency count regarding
references
0
0.2
0.4
0.6
0.8
1
Performance
Datasets
Accuracy
0
0.2
0.4
0.6
0.8
1
Boles
[31]
EER:
Emine
Krichen
…
Jong
Gook
Ko
…
Martin
Roche
…
Masek
[24]
FRR:
N.
Tajbakhsh
…
Radhika
[36]
…
Wildes
…
Ya
Ping
Haung
…
Performance
Iris patterns references
10. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
108
5. CONCLUSION
This paper presents a comprehensive review of state-of-the-art techniques in iris recognition. It
comprises of methodologies, algorithms and techniques related to this domain like feature
extraction etc. Finally, the techniques have been evaluated in terms of efficiency. Different
evaluation criteria have been employed to find the variations in the methods proposed so far in
the literature and which method is better and in what capacity. The research also provides a wide
range of other articles and average time along their algorithms, methods, procedures and
approaches performance measure. It also includes the comparison of different researches
outcomes and give a brief description about all. The survey can be a good platform for fresh and
intermediate researchers in the field of iris recognition.
REFERENCES
[1] Bowyer, K. W., & Burge, M. J. (Eds.). (2016). Handbook of iris recognition. Springer London.
[2] Hasan, O., & Tahar, S. (2015). Formal verification methods. In Encyclopedia of Information Science
and Technology, Third Edition (pp. 7162-7170). IGI Global.
[3] Harz, D., & Knottenbelt, W. (2018). Towards safer smart contracts: A survey of languages and
verification methods. arXiv preprint arXiv:1809.09805.
[4] Rajhans, A., & Krogh, B. H. (2012, April). Heterogeneous verification of cyber-physical systems
using behavior relations. In Proceedings of the 15th ACM international conference on Hybrid
Systems: Computation and Control (pp. 35-44).
[5] He, Z., Sun, Z., Tan, T., Qiu, X., Zhong, C., & Dong, W. (2008, June). Boosting ordinal features for
accurate and fast iris recognition. In 2008 IEEE Conference on Computer Vision and Pattern
Recognition (pp. 1-8). IEEE.
[6] He, Z., Tan, T., Sun, Z., & Qiu, X. (2008, October). Robust eyelid, eyelash and shadow localization
for iris recognition. In 2008 15th IEEE International Conference on Image Processing (pp. 265-268).
IEEE.
[7] Prabhakar, S., Pankanti, S., & Jain, A. K. (2003). Biometric recognition: Security and privacy
concerns. IEEE security & privacy, 99(2), 33-42.
[8] Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE
Transactions on circuits and systems for video technology, 14(1), 4-20.
[9] Thepade, S. D., & Bidwai, P. (2013, August). Iris recognition using fractional coefficients of
transforms, Wavelet Transforms and Hybrid Wavelet Transforms. In Control Computing
Communication & Materials (ICCCCM), 2013 International Conference on (pp. 1-5). IEEE.
[10] Dhage, S. S., Hegde, S. S., Manikantan, K., & Ramachandran, S. (2015). DWT-based feature
extraction and radon transform based contrast enhancement for improved iris recognition. Procedia
Computer Science, 45, 256-265.
[11] Kekre, H. B., Thepade, S. D., Jain, J., & Agrawal, N. (2011, February). Iris recognition using texture
features extracted from walshlet pyramid. In Proceedings of the International Conference &
Workshop on Emerging Trends in Technology (pp. 76-81). ACM.
[12] Suciati, N., Anugrah, A. B., Fatichah, C., Tjandrasa, H., Arifin, A. Z., Purwitasari, D., & Navastara,
D. A. (2016, October). Feature extraction using statistical moments of wavelet transform for iris
recognition. In Information & Communication Technology and Systems (ICTS), 2016 International
Conference on (pp. 193-198). IEEE.
[13] Sharma, V. P., Mishra, S. K., & Dubey, D. (2013, September). Improved Iris Recognition System
Using Wavelet Transform and Ant Colony Optimization. In Computational Intelligence and
Communication Networks (CICN), 2013 5th International Conference on (pp. 243-246). IEEE.
[14] Jadhav, T. H., & Dewan, J. H. (2016). Iris Recognition using Self Mutated Hybrid Wavelet
Transform using Cosine Haar Hartley and Slant Transforms with Partial Energies of Transformed Iris
Images. International Journal of Computer Applications (IJCA), 140, 0975-8887.
[15] Minaee, S., Abdolrashidi, A., & Wang, Y. (2015, August). Iris recognition using scattering transform
and textural features. In Signal Processing and Signal Processing Education Workshop (SP/SPE),
2015 IEEE (pp. 37-42). IEEE.
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
109
[16] Raja, K. B., Raghavendra, R., Vemuri, V. K., & Busch, C. (2015). Smartphone based visible iris
recognition using deep sparse filtering. Pattern Recognition Letters, 57, 33-42.
[17] Alvarez-Betancourt, Y., & Garcia-Silvente, M. (2016). A keypoints-based feature extraction method
for iris recognition under variable image quality conditions. Knowledge-Based Systems, 92, 169-182.
[18] Duraipandi, C., Pratap, A., & Uthariaraj, R. (2014, April). A grid based iris biometric watermarking
using wavelet transform. In Recent Trends in Information Technology (ICRTIT), 2014 International
Conference on (pp. 1-6). IEEE.
[19] Tajouri, I., Ghorbel, A., Aydi, W., & Masmoudi, N. (2016, December). An efficient iris texture
analysis based on HAAR wavelet 2D Log Gabor and monogenic filter. In Sciences and Techniques of
Automatic Control and Computer Engineering (STA), 2016 17th International Conference on (pp.
153-157). IEEE.
[20] Rahulkar, A. D., & Holambe, R. S. (2012). Partial iris feature extraction and recognition based on a
new combined directional and rotated directional wavelet filter banks. Neurocomputing, 81, 12-23.
[21] Bhateja, A. K., Sharma, S., Chaudhury, S., & Agrawal, N. (2016). Iris recognition based on sparse
representation and k-nearest subspace with genetic algorithm. Pattern Recognition Letters, 73, 13-18.
[22] Bowyer, K. W., Hollingsworth, K., & Flynn, P. J. (2008). Image understanding for iris biometrics: A
survey. Computer vision and image understanding, 110(2), 281-307.
[23] He, Z., Tan, T., & Sun, Z. (2006, August). Iris localization via pulling and pushing. In Pattern
Recognition, 2006. ICPR 2006. 18th International Conference on (Vol. 4, pp. 366-369). IEEE.
[24] De Mira, J., & Mayer, J. (2003, October). Image feature extraction for application of biometric
identification of iris-a morphological approach. In Computer Graphics and Image Processing, 2003.
SIBGRAPI 2003. XVI Brazilian Symposium on (pp. 391-398). IEEE.
[25] Guo, G., & Jones, M. J. (2008, January). Iris extraction based on intensity gradient and texture
difference. In Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on (pp. 1-6).
IEEE.
[26] Ma, L., Tan, T., Wang, Y., & Zhang, D. (2003). Personal identification based on iris texture
analysis. IEEE transactions on pattern analysis and machine intelligence, 25(12), 1519-1533.
[27] Daugman, J. (2009). How iris recognition works. In The essential guide to image processing (pp.
715-739).
[28] Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical
independence. IEEE transactions on pattern analysis and machine intelligence, 15(11), 1148-1161.
[29] de Martin-Roche, D., Sanchez-Avila, C., & Sanchez-Reillo, R. (2001, October). Iris recognition for
biometric identification using dyadic wavelet transform zero-crossing. In Security Technology, 2001
IEEE 35th International Carnahan Conference on (pp. 272-277). IEEE.
[30] Masek, L. (2003). Recognition of human iris patterns for biometric identification.
[31] Liu, X., Bowyer, K. W., & Flynn, P. J. (2005, June). Experimental evaluation of iris recognition.
In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer
Society Conference on (pp. 158-158). IEEE.
[32] Hollingsworth, K., Baker, S., Ring, S., Bowyer, K. W., & Flynn, P. J. (2009, May). Recent research
results in iris biometrics. In Optics and Photonics in Global Homeland Security V and Biometric
Technology for Human Identification VI (Vol. 7306, p. 73061Y). International Society for Optics and
Photonics.
[33] Wildes, R. P., Asmuth, J. C., Green, G. L., Hsu, S. C., Kolczynski, R. J., Matey, J. R., & McBride, S.
E. (1996). A machine-vision system for iris recognition. Machine vision and Applications, 9(1), 1-8.
[34] Wildes, R. P. (1997). Iris recognition: an emerging biometric technology. Proceedings of the
IEEE, 85(9), 1348-1363.
[35] Wildes, R. P., Asmuth, J. C., Green, G. L., Hsu, S. C., Kolczynski, R. J., Matey, J. R., & McBride, S.
E. (1994, December). A system for automated iris recognition. In Applications of Computer Vision,
1994., Proceedings of the Second IEEE Workshop on (pp. 121-128). IEEE.
[36] Krichen, E., Mellakh, M. A., Garcia-Salicetti, S., & Dorizzi, B. (2004, August). Iris identification
using wavelet packets. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th
International Conference on (Vol. 4, pp. 335-338). IEEE.
[37] Boles, W. W., & Boashash, B. (1998). A human identification technique using images of the iris and
wavelet transform. IEEE transactions on signal processing, 46(4), 1185-1188.
[38] Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient iris recognition by characterizing key local
variations. IEEE Transactions on Image processing, 13(6), 739-750.
12. International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 1, February 2022
110
[39] Ko, J. G., Gil, Y. H., Yoo, J. H., & Chung, K. I. (2010). U.S. Patent No. 7,715,594. Washington, DC:
U.S. Patent and Trademark Office.
[40] Tajbakhsh, N., Misaghian, K., & Bandari, N. M. (2009, September). A region-based iris feature
extraction method based on 2D-wavelet transform. In European Workshop on Biometrics and Identity
Management (pp. 301-307). Springer, Berlin, Heidelberg.
[41] Huang, Y. P., Luo, S. W., & Chen, E. Y. (2002). An efficient iris recognition system. In Machine
Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on (Vol. 1, pp. 450-
454). IEEE.
[42] Radhika, K. R., Sheela, S. V., Venkatesha, M. K., & Sekhar, G. N. (2009, September). Multi-modal
authentication using continuous dynamic programming. In European Workshop on Biometrics and
Identity Management (pp. 228-235). Springer, Berlin, Heidelberg.
[43] Al-Waisy, A. S., Qahwaji, R., Ipson, S., Al-Fahdawi, S., & Nagem, T. A. (2018). A multi-biometric
iris recognition system based on a deep learning approach. Pattern Analysis and Applications, 21(3),
783-802.
[44] Dua, M., Gupta, R., Khari, M., & Crespo, R. G. (2019). Biometric iris recognition using radial basis
function neural network. Soft Computing, 23(22), 11801-11815.
[45] Ahmadi, N., Nilashi, M., Samad, S., Rashid, T. A., & Ahmadi, H. (2019). An intelligent method for
iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120,
105701.
AUTHORS
Tara Othman Qadir is a PhD student in Faculty of Electrical and Electronic Engineering,
Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Johor, Malaysia. She is
a lecturer at Department of Software and Informatics, College of Engineering, Salahaddin
University, Erbil, Kurduistan, Iraq. She got her MSc in Security and BSc, in Computer
Science in Baghdad and used to be a programmer in Iraqi Commission for Computer
Informatics, Scientific Technology Information Center in Baghdad.
Dr. Nik Shahidah Afifi Md Taujuddin is a senior lecturer at Electronic Engineering
Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn
Malaysia. She obtained her PhD in Image Processing from the Universiti Tun Hussein
Onn Malaysia and her MSc and BSc (Hons) in Electrical and Electronic Engineering from
the Universiti Teknologi Malaysia. She also used to be a visiting researcher at Nagaoka
University of Technology, Japan. Her research area is Image Processing, Computer
Security and Computer Networks.
Sundas Naqeeb Khan is a PhD scholar at Faculty of Computer Science and Information
Technology, Universiti Tun Hussein Onn Malaysia. She obtained her MSCS degree with
distinction from The University of Lahore, Pakistan and her MSc from Fatima Jinnah
Women University, Pakistan. She also used to be a visiting lecturer in The University of
Punjab, Pakistan, and Mirpur University of Science and Technology, Pakistan. Her
research area is multi-disciplinary optimization, e-commerce, image processing, database
management, data mining, text mining, and electrical engineering.