This study analyzed fingerprint data collected in 2010 to investigate the effect of force levels on fingerprint recognition performance. Fingerprint data was collected from 154 subjects across different force levels (5N, 7N, 9N, 11N, 13N) and analyzed using detection error tradeoff curves and zoo plots. The results showed that higher force levels generally had lower error rates than lower force levels. Index fingers showed better performance than other fingers, while little fingers had the highest error rates. Analysis of performance by force level and finger can provide useful insights to optimize fingerprint recognition systems.
Stability of Individuals in a Fingerprint System across Force LevelsITIIIndustries
This research studied the question: “Are all
individual’s performance stable in a fingerprint recognition
system?” The fingerprints of 154 individuals, provided at
different force levels, were examined using the biometric
menagerie tool, first coined by Doddington et al. in 1998. The
Biometric Menagerie illustrates how each person in a given
dataset performs in a biometric system, by using their genuine
and impostor scores, and providing them a classification based
upon those scores. This research examined the biometric
menagerie classifications across different force levels in a
fingerprint recognition study to uncover if individuals performed
the same over five force levels. The study concluded that they did
not, and a new metric has been created to quantify this
phenomenon. As a result of this discovery, the new metric,
Stability Score Index is described to showcase the movement of
individuals in the menagerie.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
Stability of Individuals in a Fingerprint System across Force LevelsITIIIndustries
This research studied the question: “Are all
individual’s performance stable in a fingerprint recognition
system?” The fingerprints of 154 individuals, provided at
different force levels, were examined using the biometric
menagerie tool, first coined by Doddington et al. in 1998. The
Biometric Menagerie illustrates how each person in a given
dataset performs in a biometric system, by using their genuine
and impostor scores, and providing them a classification based
upon those scores. This research examined the biometric
menagerie classifications across different force levels in a
fingerprint recognition study to uncover if individuals performed
the same over five force levels. The study concluded that they did
not, and a new metric has been created to quantify this
phenomenon. As a result of this discovery, the new metric,
Stability Score Index is described to showcase the movement of
individuals in the menagerie.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODSsipij
Researches based on animal detection plays a very vital role in many real life applications. Applications
which are very important are preventing animal vehicle collision on roads, preventing dangerous animal
intrusion in residential area, knowing locomotive behavioural of targeted animal and many more. There
are limited areas of research related to animal detection. In this paper we will discuss some of these areas
for detection of animals.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
Published in The 7th International Conference on Information Technology and Applications (ICITA 2011), Sydney Australia, 21 Nov - 24 Nov 2011
The purpose of this paper is to provide additional analysis of image quality and Henry Classification on Finger location on a single sensor. One hundred and sixty nine individuals provided six impressions of their left index, left middle, right index, and right middle fingers. The results show that there is significant difference in image quality, Henry classifications, and zoo animal distribution across the four finger locations under study. The results of this research show that location is an important consideration when developing enrollment best practices for single print systems.
This is a follow on paper to which examined the impact of gender on a fingerprint recognition system. In that study, the authors used two different technologies (capacitance and optical) single finger sensors. In this study, the authors examined the differences in gender using images automatically segmented from a 10-print fingerprint sensor. Therefore, we analyze in detail the fingerprint locations and assess the performance, image quality and Henry Classification. Our results concur with recent literature which shows no significant difference in Henry Classification across gender, although there is a significant difference across the different fingerprint locations. We do show that there is a difference in image quality (females averaging 81.929 and males averaging 84.196), with a resulting difference in performance. The female dataset performed at an Equal Error Rate of 0.42%, and the male dataset performing at an EER of 0.68%).
According to a report by Frost and Sullivan in 2007, revenues for non-AFIS fingerprint devices in notebook PC's and wireless devices is anticipated to grow from $148.5 million to $1588.0 million by 2014, a compound annual growth rate of 40.3% [1]. The AFIS market has a compound annual growth rate of 15.2% with revenues of $445.0 million in 2007. With the development of mobile applications in a number of different market segments, such as healthcare, retail, and law enforcement, this paper analyzed the performance of fingerprints of different sizes, from different sensors...
Published in ICITA 2013, 8th International Conference on Information Technology and Applications, Sydney, Australia, 1 - 4 July, 2013
The amount of time taken to enroll or collect data from a subject in a fingerprint recognition system is of paramount importance. Time taken directly affects cost. A trade-off between number of impressions collected and number of interaction attempts allowed to submit those impressions must be realized. In this experiment, data were collected using an optical fingerprint sensor. Each subject submitted six successful impressions with a maximum of 18 interaction attempts. The resulting images were analyzed using three methods: the number of interaction attempts per finger, quality differences from the first three impressions to the last three impressions, and finally matching performance from the first three impressions to the last three impressions. The right middle finger seemed to have the most issues collecting as it required the most interaction attempts. Analysis was performed to show no significant differences in image quality or matching performance. However, after further analysis, a steady improvement was noticed from Group A to Group B in both image quality and matching performance.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and methodologies for the extraction of features are also discussed.
novel method of identifying fingerprint using minutiae matching in biometric ...INFOGAIN PUBLICATION
Fingerprint is one of the best apparatus to identify human because of its uniqueness, details information, hard to change and long-term indicators of human identity where there are several biometric feature that can be recycled to endorse the individuality. Identification of fingerprint is very important in forensic science, trace any part of human, collection of crime part and proof from a crime. This paper presents a new method of identifying fingerprint in biometrics security system. Fingerprint is one of the best example in biometric security because it can identify personal information and it is much secure than any other biometric identification system. The experimental result exhibits the performance of the proposed method.
Vision based entomology how to effectively exploit color and shape featurescseij
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of
agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the
diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various kinds
of interactions between humans and insects. There have been several attempts to create a method to
perform insect identification accurately. Great knowledge and experience on entomology are required for
accurate insect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. We propose an automatic insect identification framework that can identify
grasshoppers and butterflies from colored images. Two classes of insects are chosen for a proof-ofconcept.
Classification is achieved by manipulating insects’ color and their shape feature since each class
of sample case has different color and distinctive body shapes. The proposed insect identification process
starts by extracting features from samples and splitting them into two training sets. One training
emphasizes on computing RGB features while the other one is normalized to estimate the area of binary
color that signifies the shape of the insect. SVM classifier is used to train the data obtained. Final decision
of the classifier combines the result of these two features to determine which class an unknown instance
belong to. The preliminary results demonstrate the efficacy and efficiency of our two-step automatic insect
identification approach and motivate us to extend this framework to identify a variety of other species of
insects.
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODSsipij
Researches based on animal detection plays a very vital role in many real life applications. Applications
which are very important are preventing animal vehicle collision on roads, preventing dangerous animal
intrusion in residential area, knowing locomotive behavioural of targeted animal and many more. There
are limited areas of research related to animal detection. In this paper we will discuss some of these areas
for detection of animals.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
Published in The 7th International Conference on Information Technology and Applications (ICITA 2011), Sydney Australia, 21 Nov - 24 Nov 2011
The purpose of this paper is to provide additional analysis of image quality and Henry Classification on Finger location on a single sensor. One hundred and sixty nine individuals provided six impressions of their left index, left middle, right index, and right middle fingers. The results show that there is significant difference in image quality, Henry classifications, and zoo animal distribution across the four finger locations under study. The results of this research show that location is an important consideration when developing enrollment best practices for single print systems.
This is a follow on paper to which examined the impact of gender on a fingerprint recognition system. In that study, the authors used two different technologies (capacitance and optical) single finger sensors. In this study, the authors examined the differences in gender using images automatically segmented from a 10-print fingerprint sensor. Therefore, we analyze in detail the fingerprint locations and assess the performance, image quality and Henry Classification. Our results concur with recent literature which shows no significant difference in Henry Classification across gender, although there is a significant difference across the different fingerprint locations. We do show that there is a difference in image quality (females averaging 81.929 and males averaging 84.196), with a resulting difference in performance. The female dataset performed at an Equal Error Rate of 0.42%, and the male dataset performing at an EER of 0.68%).
According to a report by Frost and Sullivan in 2007, revenues for non-AFIS fingerprint devices in notebook PC's and wireless devices is anticipated to grow from $148.5 million to $1588.0 million by 2014, a compound annual growth rate of 40.3% [1]. The AFIS market has a compound annual growth rate of 15.2% with revenues of $445.0 million in 2007. With the development of mobile applications in a number of different market segments, such as healthcare, retail, and law enforcement, this paper analyzed the performance of fingerprints of different sizes, from different sensors...
Published in ICITA 2013, 8th International Conference on Information Technology and Applications, Sydney, Australia, 1 - 4 July, 2013
The amount of time taken to enroll or collect data from a subject in a fingerprint recognition system is of paramount importance. Time taken directly affects cost. A trade-off between number of impressions collected and number of interaction attempts allowed to submit those impressions must be realized. In this experiment, data were collected using an optical fingerprint sensor. Each subject submitted six successful impressions with a maximum of 18 interaction attempts. The resulting images were analyzed using three methods: the number of interaction attempts per finger, quality differences from the first three impressions to the last three impressions, and finally matching performance from the first three impressions to the last three impressions. The right middle finger seemed to have the most issues collecting as it required the most interaction attempts. Analysis was performed to show no significant differences in image quality or matching performance. However, after further analysis, a steady improvement was noticed from Group A to Group B in both image quality and matching performance.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advances in techniques, algorithms, qualified classifiers, datasets and methodologies for the extraction of features are also discussed.
novel method of identifying fingerprint using minutiae matching in biometric ...INFOGAIN PUBLICATION
Fingerprint is one of the best apparatus to identify human because of its uniqueness, details information, hard to change and long-term indicators of human identity where there are several biometric feature that can be recycled to endorse the individuality. Identification of fingerprint is very important in forensic science, trace any part of human, collection of crime part and proof from a crime. This paper presents a new method of identifying fingerprint in biometrics security system. Fingerprint is one of the best example in biometric security because it can identify personal information and it is much secure than any other biometric identification system. The experimental result exhibits the performance of the proposed method.
Vision based entomology how to effectively exploit color and shape featurescseij
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of
agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the
diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various kinds
of interactions between humans and insects. There have been several attempts to create a method to
perform insect identification accurately. Great knowledge and experience on entomology are required for
accurate insect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. We propose an automatic insect identification framework that can identify
grasshoppers and butterflies from colored images. Two classes of insects are chosen for a proof-ofconcept.
Classification is achieved by manipulating insects’ color and their shape feature since each class
of sample case has different color and distinctive body shapes. The proposed insect identification process
starts by extracting features from samples and splitting them into two training sets. One training
emphasizes on computing RGB features while the other one is normalized to estimate the area of binary
color that signifies the shape of the insect. SVM classifier is used to train the data obtained. Final decision
of the classifier combines the result of these two features to determine which class an unknown instance
belong to. The preliminary results demonstrate the efficacy and efficiency of our two-step automatic insect
identification approach and motivate us to extend this framework to identify a variety of other species of
insects.
Biometrics is the science and technology of
measuring and analyzing biological data. In information
technology, biometrics refers to technologies that measure and
analyze human body characteristics, such as DNA, fingerprints,
eye retinas and irises, voice patterns, facial patterns and hand
measurements, for authentication purposes. This paper is about
the applications of biometric especially in the field of healthcare
and its future uses
Fingerprint recognition is an important
biometric technology, and its use is increasing day by day.
Fingerprint recognition is affected by several physiological
factors like age, wear and tear of skin, and technological
factors like sensor technologies. This paper builds on
previous research in the area of gender differences in
fingerprint features, and reports results of differences in
performance of fingerprints collected from males and
females. The researchers propose a fingerprint analysis
framework for testing differences in gender and apply the
framework to fingerprints collected from males and
females.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONijaia
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among
human lives. The advancements undertaken to make designs with human similarities or adaptations to the
society are elaborated in detail. The increasing manufacturing and use of robots for industrial purposes
have been related to their operating mechanisms. The experiments and laboratory testing of these devices
is analysed in form tables to show the statistical side of the technology. This report explains the
technological aspects and laboratory experiments that have been advanced to increase knowledge on these
digital technologies. This study aims to present an overview of two developing technologies: artificial
intelligence (AI) and robots and their potential applications. The product variety is a primary
characteristic of each of these specialties. In addition, they may be described as disruptive, facilitating,
and transdisciplinary.