This document summarizes a research paper on reducing the process time for fingerprint identification systems. It discusses how fingerprints are currently classified and matched in large databases, which can be slow. The paper proposes classifying fingerprints by pattern at enrollment to speed up matching by first narrowing the search space. It aims to improve on current methods used by forensic divisions with large databases by taking fingerprint features like singular points into account during classification and matching.
Reducing Process-Time for Fingerprint Identification SystemCSCJournals
Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. This paper presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups at the time of enrollment, and improves fingerprint matching while matching the input template with stored template. To solve the problem, we take several aspects into consideration like classification of fingerprint, singular points. The algorithm result indicates that this approach manages to speed up the matching effectively, and therefore prove to be suitable for large database like forensic divisions.
Biometric Fingerprint Recognintion based on Minutiae MatchingNabila mahjabin
The document summarizes a student's project on biometric fingerprint recognition based on minutiae matching. It includes an introduction to fingerprints and fingerprint recognition techniques. The project involves developing a complete fingerprint recognition system through minutiae extraction and matching. The system applies preprocessing techniques like image enhancement and binarization before extracting minutiae features from fingerprints. It then performs minutiae marking and false minutiae removal before matching fingerprints based on their minutiae patterns. The performance of the developed system is evaluated on a fingerprint database.
International Journal of Computational Engineering Research(IJCER)ijceronline
1. The document proposes a palm vein authentication method using junction points and correlation. It extracts junction points from palm vein images as features for identification.
2. Key steps include preprocessing images through segmentation and noise removal. Junction points are then identified and used to authenticate individuals by comparing points through correlation.
3. The method aims to reduce processing time and increase accuracy compared to previous palm vein authentication research. It argues junction points provide a new biometric feature for identification.
This is a Fingerprint based class attendance system in higher institutions, The implementation take attendance of student in a class and give output of student eligibility status at the end of the semester or term
Correlation based Fingerprint Recognitionmahesamrin
The document discusses fingerprint identification and analysis. It describes the basic ridge and valley patterns that make up fingerprints, including three main types of patterns called singularities. It then provides a brief history of the use of fingerprints for identification. It discusses two main fingerprint matching methods - minutiae matching which extracts and compares distinctive points on fingerprints, and correlation-based matching which compares entire fingerprint template regions.
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.
Fingerprint Minutiae Extraction and Compression using LZW Algorithmijsrd.com
For security and surveillance automated personal identification is major issue. We can see a lot varieties of biometric systems like face detection, fingerprint recognition, iris recognition, voice recognition, palm recognition etc. In our project we will only go for fingerprint recognition. Never two peoples have exactly same fingerprints even twins, they are totally unique. The sensors capture the finger prints of humans and convert them into images and a minutiae extraction algorithm extracts the location of minutiae points called termination and bifurcation. A database system stores these patterns and minutiae points of fingerprint. A large storage space required to store bifurcation and termination points for the fingerprint database. LZW compression algorithm has been used to reduce the size of data. With LZW applied on these extracted minutiae points, these minutiae points get encoded which add more security feature.
Reducing Process-Time for Fingerprint Identification SystemCSCJournals
Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. This paper presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups at the time of enrollment, and improves fingerprint matching while matching the input template with stored template. To solve the problem, we take several aspects into consideration like classification of fingerprint, singular points. The algorithm result indicates that this approach manages to speed up the matching effectively, and therefore prove to be suitable for large database like forensic divisions.
Biometric Fingerprint Recognintion based on Minutiae MatchingNabila mahjabin
The document summarizes a student's project on biometric fingerprint recognition based on minutiae matching. It includes an introduction to fingerprints and fingerprint recognition techniques. The project involves developing a complete fingerprint recognition system through minutiae extraction and matching. The system applies preprocessing techniques like image enhancement and binarization before extracting minutiae features from fingerprints. It then performs minutiae marking and false minutiae removal before matching fingerprints based on their minutiae patterns. The performance of the developed system is evaluated on a fingerprint database.
International Journal of Computational Engineering Research(IJCER)ijceronline
1. The document proposes a palm vein authentication method using junction points and correlation. It extracts junction points from palm vein images as features for identification.
2. Key steps include preprocessing images through segmentation and noise removal. Junction points are then identified and used to authenticate individuals by comparing points through correlation.
3. The method aims to reduce processing time and increase accuracy compared to previous palm vein authentication research. It argues junction points provide a new biometric feature for identification.
This is a Fingerprint based class attendance system in higher institutions, The implementation take attendance of student in a class and give output of student eligibility status at the end of the semester or term
Correlation based Fingerprint Recognitionmahesamrin
The document discusses fingerprint identification and analysis. It describes the basic ridge and valley patterns that make up fingerprints, including three main types of patterns called singularities. It then provides a brief history of the use of fingerprints for identification. It discusses two main fingerprint matching methods - minutiae matching which extracts and compares distinctive points on fingerprints, and correlation-based matching which compares entire fingerprint template regions.
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.
Fingerprint Minutiae Extraction and Compression using LZW Algorithmijsrd.com
For security and surveillance automated personal identification is major issue. We can see a lot varieties of biometric systems like face detection, fingerprint recognition, iris recognition, voice recognition, palm recognition etc. In our project we will only go for fingerprint recognition. Never two peoples have exactly same fingerprints even twins, they are totally unique. The sensors capture the finger prints of humans and convert them into images and a minutiae extraction algorithm extracts the location of minutiae points called termination and bifurcation. A database system stores these patterns and minutiae points of fingerprint. A large storage space required to store bifurcation and termination points for the fingerprint database. LZW compression algorithm has been used to reduce the size of data. With LZW applied on these extracted minutiae points, these minutiae points get encoded which add more security feature.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
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.
Fingerprint Recognition Using Minutiae Based and Discrete Wavelet TransformAM Publications
Fingerprint recognition is one of the methods used in biometric system. Most of the biometric systems which are used for human identification or person’s identification. In this paper we are discussing minutiae matching and discrete wavelet transform and comparison of these two in fingerprint recognition. In this paper, firstly it uses fingerprint identification and performance in terms of equal error rate and then by calculating using discrete wavelet transform. The main aim of this paper is to create performing and accurate program for fingerprint identification.
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.
Experimental study of minutiae based algorithm for fingerprint matchingcsandit
In this paper, a minutiae-based algorithm for fingerprint pattern recognition and matching is
proposed. The algorithm uses the distance between the minutiae and core points to determine
the pattern matching scores for fingerprint images. Experiments were conducted using
FVC2002 fingerprint database comprising four datasets of images of different sources and
qualities. False Match Rate (FMR), False Non-Match Rate (FNMR) and the Average Matching
Time (AMT) were the statistics generated for testing and measuring the performance of the
proposed algorithm. The comparative analysis of the proposed algorithm and some existing
minutiae based algorithms was carried out as well. The findings from the experimental study
were presented, interpreted and some conclusions were drawn.
The document proposes a reliable fingerprint matching system using filter-based and Euclidean distance algorithms. It aims to improve accuracy of fingerprint matching by addressing issues caused by fingertip surface conditions and image quality. The proposed system extracts minutiae points using Gabor filters and matches fingerprints based on minutiae configuration and pore distances calculated using k-nearest neighbors algorithm. Testing on 20 fingerprints showed an average matching accuracy of 95-99% using this approach.
Latent fingerprint and vein matching using ridge feature identificationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Biometric system works on behavioral and physiological biometric parameters to spot a person. Every fingerprint contains distinctive options and its recognizing system primarily works on native ridge feature local ridge endings, minutiae, core point, delta, etc. However, fingerprint pictures have poor quality thanks to variations in skin and impression conditions. In personal identification, fingerprint recognition is taken into account the foremost outstanding and reliable technique for matching with keep fingerprints within the information. Minutiae extraction is additional essential step in fingerprint matching. This paper provides plan regarding numerous feature extraction and matching algorithms for fingerprint recognition systems and to seek out that technique is additional reliable and secure.
A Survey Based on Fingerprint Matching SystemIJTET Journal
Abstract — Fingerprint is one of the biometric features mostly used for identification and verification. Latent fingerprints are conventionally recovered coming in to existence of crime scenes and are analyzed with active databases of well-known fingerprints for finding criminals. A bulk of matching algorithms with distant uniqueness has been developed in modern years and the algorithms are depending up on minutiae features. The detection of accepted systems tries to find which fingerprint in a database matches the fingerprint needs the matching of its minutiae against the input fingerprint. Since the detection complexity are more minutiae of other fingerprints. Therefore, fingerprint matching system is a higher than verification and detection systems. This paper discussed about the various novel techniques like Minutia Cylinder Code (MCC) algorithm, Minutia score matching and Graphic Processing Unit (GPU). The feature extraction anywhere in the extracted features is sovereign of shift and rotation of the fingerprint. Meanwhile, the matching operation is performed much more easily and higher accuracy.
The process of matching fingerprints is carried out based on the minutiae features found in a fingerprint. Two cases are considered in matching: 1. One to One matching, 2. One to Many matching
Study of Local Binary Pattern for Partial Fingerprint IdentificationIJMER
Fingerprints are usually used in recognition of a person's identity because of its uniqueness,
stability. Today also the matching of incomplete or partial fingerprints remains challenge. The current
technology is somewhat mature for matching ten prints, but matching of partial fingerprints still needs
a lot of improvement. Automatic fingerprint identification techniques have been successfully adapted to
both civilian and forensic applications. But this Fingerprint identification system suffers from the
problem of handling incomplete prints and discards any partial fingerprints obtained. Level 2 features
are very efficient if the quality of achievement decreases the number of level 2 features will not be
enough for establishing high accuracy in identification. In such cases pores (level 3 features) can be
used for partial fingerprint matching with the help of suitable technique local binary pattern features.
Local binary pattern feature is used to match the pore against with full fingerprints. The first step
involves extracting the pores from the partial image. These pores act as anchor points and sub window
(32*32) is formed surrounding the pores. Then rotation invariant LBP histograms are obtained from
the surrounding window. Finally chi-square formula is used to calculate the minimum distance between
two histograms to find best matching score
Fingerprint recognition using minutiae based featurevarsha mohite
Fingerprint recognition techniques can be categorized as minutiae-based or correlation-based. Minutiae-based techniques extract and match minutiae points like ridge endings and bifurcations. This approach has difficulties with low quality fingerprints. Correlation-based techniques match ridge patterns but require precise image registration. Fingerprint matching algorithms first enhance images, extract minutiae points, and compare points between images to determine a match.
This Powerpoint prsentation contains information about the overview of various successful works performed for Biometric Recognition using Deep Learning. This work is based on an existing survey paper.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
This document presents a bimodal biometric system that fuses fingerprint and iris features for identification. It extracts features from the iris using two-level discrete wavelet transformation and discrete cosine transformation. Fingerprint features are extracted using fast Fourier transformation and discrete wavelet transformation. The iris and fingerprint features are concatenated to form the final feature set. Experimental results on fingerprint and iris databases show that the proposed bimodal system has lower false rejection and false acceptance rates and higher total success rate compared to existing unimodal systems.
This document describes a technique for human iris recognition for biometric identification. It involves 6 major steps: image acquisition, localization, isolation, normalization, feature extraction, and matching. The iris is localized by detecting the pupil and outer iris boundaries using techniques like Canny edge detection and Hough transforms. The iris region is then isolated using masking. It is normalized and represented as a fixed-sized block. Features are extracted using techniques like Gabor filters and Haar wavelets to generate biometric templates. Templates are matched using Hamming distance to identify individuals in applications like border control, computer login, and financial transactions. The iris has properties that make it suitable and accurate for identification compared to other biometrics.
Comparative study of various enhancement techniques for finger print imagesMade Artha
The document discusses various techniques for enhancing fingerprint images. It begins by explaining how fingerprints are used for biometric identification but that fingerprint images are often degraded, requiring enhancement techniques prior to minutiae extraction. The purpose of the study is to implement and evaluate different enhancement techniques on synthetic and real fingerprint images. It discusses how enhancement improves image quality and reliability of minutiae extraction, which is important for fingerprint-based identification and verification applications. The document also provides details on enrollment and identification processes using fingerprint biometrics.
Review on vein enhancement methods for biometric systemeSAT Journals
Abstract
Vein biometric system uses vein inside human body as a unique identification. Researchers have concluded that human vein
pattern is unique to an individual. Vein pattern cannot be stolen or duplicated because it is in the human body. At present, vein
pattern in finger, palm, palm-dorsa and wrist of human are used for biometric system. This paper presents a review on vein
enhancement methods. This paper begins with overview of vein detection and the advantages of vein as biometric modal. Next, the
vein capturing technology to obtain the vein pattern in human body is presented. Finally, preceding works related to vein
enhancement methods are discussed and reviewed
Keywords: biometric, vein, vein detection, vein enhancement, vein pattern.
This document discusses fingerprint recognition. It begins by defining fingerprint recognition as the automated process of verifying a match between two fingerprints. Fingerprints are a form of biometrics used to identify individuals due to their uniqueness. The document then discusses how fingerprints are distinguished by features called minutiae, specifically ridge endings and bifurcations. It also outlines some common fingerprint matching techniques such as correlation-based and minutiae-based matching.
El documento presenta una serie de deseos para el año nuevo que son rechazados uno a uno por considerarse poco realistas. Finalmente, se desea a las buenas personas que tengan la fuerza para trabajar por un mundo mejor con paz, justicia e igualdad, aunque parezca imposible, ya que construir un mundo más sano y justo socialmente es el mejor objetivo.
1. Vikram Sarabhai was a pioneering Indian scientist who established the Physical Research Laboratory (PRL) in Ahmedabad in 1947 to conduct research on cosmic rays, aeronomy, and space sciences.
2. As a young scientist in the 1940s, Sarabhai conducted research on cosmic rays and their small time variations using sophisticated instrumentation at PRL and other locations.
3. Cosmic rays consist of energetic protons and heavier particles that originate from our galaxy and lose their directionality after interacting with magnetic fields in interstellar space. Their variations in intensity over time and space can provide information on electromagnetic conditions in the solar system.
Reduction of False Acceptance Rate Using Cross Validation for Fingerprint Rec...IJTET Journal
Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.
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.
Fingerprint Recognition Using Minutiae Based and Discrete Wavelet TransformAM Publications
Fingerprint recognition is one of the methods used in biometric system. Most of the biometric systems which are used for human identification or person’s identification. In this paper we are discussing minutiae matching and discrete wavelet transform and comparison of these two in fingerprint recognition. In this paper, firstly it uses fingerprint identification and performance in terms of equal error rate and then by calculating using discrete wavelet transform. The main aim of this paper is to create performing and accurate program for fingerprint identification.
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.
Experimental study of minutiae based algorithm for fingerprint matchingcsandit
In this paper, a minutiae-based algorithm for fingerprint pattern recognition and matching is
proposed. The algorithm uses the distance between the minutiae and core points to determine
the pattern matching scores for fingerprint images. Experiments were conducted using
FVC2002 fingerprint database comprising four datasets of images of different sources and
qualities. False Match Rate (FMR), False Non-Match Rate (FNMR) and the Average Matching
Time (AMT) were the statistics generated for testing and measuring the performance of the
proposed algorithm. The comparative analysis of the proposed algorithm and some existing
minutiae based algorithms was carried out as well. The findings from the experimental study
were presented, interpreted and some conclusions were drawn.
The document proposes a reliable fingerprint matching system using filter-based and Euclidean distance algorithms. It aims to improve accuracy of fingerprint matching by addressing issues caused by fingertip surface conditions and image quality. The proposed system extracts minutiae points using Gabor filters and matches fingerprints based on minutiae configuration and pore distances calculated using k-nearest neighbors algorithm. Testing on 20 fingerprints showed an average matching accuracy of 95-99% using this approach.
Latent fingerprint and vein matching using ridge feature identificationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Till now many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper consists of information of about fingerprint (biometrics) recognition. The novel algorithm is considered for thinning process. Whole process of recognition is explained from image capturing to verification. The image captured is first converted to gray scale then image enrichment is done then thinning process take over charge which is main process then last process which is also equally important as thinning process is feature extraction which extracts ridge ending, bifurcation, and dot. The accuracy depends on the result of the three main process namely pre-processing, thinning process and feature extraction. Keywords: Arch, loop, whorl, Preprocessing, Thinning Process, Feature Extraction, Ridge.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Biometric system works on behavioral and physiological biometric parameters to spot a person. Every fingerprint contains distinctive options and its recognizing system primarily works on native ridge feature local ridge endings, minutiae, core point, delta, etc. However, fingerprint pictures have poor quality thanks to variations in skin and impression conditions. In personal identification, fingerprint recognition is taken into account the foremost outstanding and reliable technique for matching with keep fingerprints within the information. Minutiae extraction is additional essential step in fingerprint matching. This paper provides plan regarding numerous feature extraction and matching algorithms for fingerprint recognition systems and to seek out that technique is additional reliable and secure.
A Survey Based on Fingerprint Matching SystemIJTET Journal
Abstract — Fingerprint is one of the biometric features mostly used for identification and verification. Latent fingerprints are conventionally recovered coming in to existence of crime scenes and are analyzed with active databases of well-known fingerprints for finding criminals. A bulk of matching algorithms with distant uniqueness has been developed in modern years and the algorithms are depending up on minutiae features. The detection of accepted systems tries to find which fingerprint in a database matches the fingerprint needs the matching of its minutiae against the input fingerprint. Since the detection complexity are more minutiae of other fingerprints. Therefore, fingerprint matching system is a higher than verification and detection systems. This paper discussed about the various novel techniques like Minutia Cylinder Code (MCC) algorithm, Minutia score matching and Graphic Processing Unit (GPU). The feature extraction anywhere in the extracted features is sovereign of shift and rotation of the fingerprint. Meanwhile, the matching operation is performed much more easily and higher accuracy.
The process of matching fingerprints is carried out based on the minutiae features found in a fingerprint. Two cases are considered in matching: 1. One to One matching, 2. One to Many matching
Study of Local Binary Pattern for Partial Fingerprint IdentificationIJMER
Fingerprints are usually used in recognition of a person's identity because of its uniqueness,
stability. Today also the matching of incomplete or partial fingerprints remains challenge. The current
technology is somewhat mature for matching ten prints, but matching of partial fingerprints still needs
a lot of improvement. Automatic fingerprint identification techniques have been successfully adapted to
both civilian and forensic applications. But this Fingerprint identification system suffers from the
problem of handling incomplete prints and discards any partial fingerprints obtained. Level 2 features
are very efficient if the quality of achievement decreases the number of level 2 features will not be
enough for establishing high accuracy in identification. In such cases pores (level 3 features) can be
used for partial fingerprint matching with the help of suitable technique local binary pattern features.
Local binary pattern feature is used to match the pore against with full fingerprints. The first step
involves extracting the pores from the partial image. These pores act as anchor points and sub window
(32*32) is formed surrounding the pores. Then rotation invariant LBP histograms are obtained from
the surrounding window. Finally chi-square formula is used to calculate the minimum distance between
two histograms to find best matching score
Fingerprint recognition using minutiae based featurevarsha mohite
Fingerprint recognition techniques can be categorized as minutiae-based or correlation-based. Minutiae-based techniques extract and match minutiae points like ridge endings and bifurcations. This approach has difficulties with low quality fingerprints. Correlation-based techniques match ridge patterns but require precise image registration. Fingerprint matching algorithms first enhance images, extract minutiae points, and compare points between images to determine a match.
This Powerpoint prsentation contains information about the overview of various successful works performed for Biometric Recognition using Deep Learning. This work is based on an existing survey paper.
Bimodal Biometric System using Multiple Transformation Features of Fingerprin...IDES Editor
This document presents a bimodal biometric system that fuses fingerprint and iris features for identification. It extracts features from the iris using two-level discrete wavelet transformation and discrete cosine transformation. Fingerprint features are extracted using fast Fourier transformation and discrete wavelet transformation. The iris and fingerprint features are concatenated to form the final feature set. Experimental results on fingerprint and iris databases show that the proposed bimodal system has lower false rejection and false acceptance rates and higher total success rate compared to existing unimodal systems.
This document describes a technique for human iris recognition for biometric identification. It involves 6 major steps: image acquisition, localization, isolation, normalization, feature extraction, and matching. The iris is localized by detecting the pupil and outer iris boundaries using techniques like Canny edge detection and Hough transforms. The iris region is then isolated using masking. It is normalized and represented as a fixed-sized block. Features are extracted using techniques like Gabor filters and Haar wavelets to generate biometric templates. Templates are matched using Hamming distance to identify individuals in applications like border control, computer login, and financial transactions. The iris has properties that make it suitable and accurate for identification compared to other biometrics.
Comparative study of various enhancement techniques for finger print imagesMade Artha
The document discusses various techniques for enhancing fingerprint images. It begins by explaining how fingerprints are used for biometric identification but that fingerprint images are often degraded, requiring enhancement techniques prior to minutiae extraction. The purpose of the study is to implement and evaluate different enhancement techniques on synthetic and real fingerprint images. It discusses how enhancement improves image quality and reliability of minutiae extraction, which is important for fingerprint-based identification and verification applications. The document also provides details on enrollment and identification processes using fingerprint biometrics.
Review on vein enhancement methods for biometric systemeSAT Journals
Abstract
Vein biometric system uses vein inside human body as a unique identification. Researchers have concluded that human vein
pattern is unique to an individual. Vein pattern cannot be stolen or duplicated because it is in the human body. At present, vein
pattern in finger, palm, palm-dorsa and wrist of human are used for biometric system. This paper presents a review on vein
enhancement methods. This paper begins with overview of vein detection and the advantages of vein as biometric modal. Next, the
vein capturing technology to obtain the vein pattern in human body is presented. Finally, preceding works related to vein
enhancement methods are discussed and reviewed
Keywords: biometric, vein, vein detection, vein enhancement, vein pattern.
This document discusses fingerprint recognition. It begins by defining fingerprint recognition as the automated process of verifying a match between two fingerprints. Fingerprints are a form of biometrics used to identify individuals due to their uniqueness. The document then discusses how fingerprints are distinguished by features called minutiae, specifically ridge endings and bifurcations. It also outlines some common fingerprint matching techniques such as correlation-based and minutiae-based matching.
El documento presenta una serie de deseos para el año nuevo que son rechazados uno a uno por considerarse poco realistas. Finalmente, se desea a las buenas personas que tengan la fuerza para trabajar por un mundo mejor con paz, justicia e igualdad, aunque parezca imposible, ya que construir un mundo más sano y justo socialmente es el mejor objetivo.
1. Vikram Sarabhai was a pioneering Indian scientist who established the Physical Research Laboratory (PRL) in Ahmedabad in 1947 to conduct research on cosmic rays, aeronomy, and space sciences.
2. As a young scientist in the 1940s, Sarabhai conducted research on cosmic rays and their small time variations using sophisticated instrumentation at PRL and other locations.
3. Cosmic rays consist of energetic protons and heavier particles that originate from our galaxy and lose their directionality after interacting with magnetic fields in interstellar space. Their variations in intensity over time and space can provide information on electromagnetic conditions in the solar system.
El documento habla sobre las bibliotecas, su historia, función y algunas frases célebres sobre los libros, la lectura y las bibliotecas. Menciona que las bibliotecas surgieron hace más de 4,000 años como archivos en templos mesopotámicos para guardar registros religiosos, políticos y administrativos. También incluye varias citas famosas sobre la importancia y el placer de la lectura y el valor de los libros y las bibliotecas.
The document discusses best practices for creating a norm of clearing snow from sidewalks after winter storms. It outlines that municipal ordinances often require abutting property owners to clear sidewalks, but responsibility is sometimes unclear, leaving sidewalks unshoveled. It recommends coalition building between cities and concerned groups, as well as enforcement of fines, encouragement through contests, assistance for those unable to shovel, and policies addressing access to transit during snowy weather.
A Evolução da Publicidade nos Meios Digitaiskalledonian
O documento descreve a evolução da publicidade nos meios digitais ao longo de 7 etapas: 1) Surgimento da internet e da publicidade online; 2) Experimentação de novas técnicas de anúncios na web; 3) Busca pela atenção do receptor; 4) Respeito aos interesses do receptor; 5) Adoção de tecnologias para redes sociais; 6) Domínio da tecnologia sobre práticas nas redes sociais; 7) Discussão sobre o futuro cenário da publicidade digital.
Los moluscos son animales blandos que pueden tener conchas. Se clasifican en cinco clases: gasterópodos, bivalvos, cefalópodos, poliplacóforos y escafópodos. Los moluscos incluyen caracoles, almejas, pulpos y calamares, y viven tanto en ambientes terrestres como marinos.
Fingerprint Feature Extraction, Identification and Authentication: A Reviewpaperpublications3
Abstract: In the modern computerized world, due to high demand on fingerprint identification system, a lot of challenges keep arising in each phase of system, which include fingerprint image enhancement, feature extraction, features matching and fingerprint classification. Applications such as online banking and online shopping use techniques that depend on personal identification numbers, keys, or passwords. But there is the risk of data being forgotten, lost, or even stolen. One of the solutions to it may be biometric authentication methods which provide a unique way to identify, recognize and authenticate people. Fingerprints being the oldest methods of biometric authentication, are being explored at large. The main focus of the paper is to review fingerprint feature extraction, identification and authentication in different image/pattern based and minutiae-based fingerprints.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
This presentation provides an overview of fingerprint scanners. It discusses the history of fingerprint recognition from the 1800s to modern day uses in mobile phones. The general structure of fingerprint scanners is described, including optical and capacitive technologies. Fingerprint patterns like loops, whorls and arches are defined, along with features used for identification. Fingerprint matching techniques including minutiae-based and correlation-based approaches are covered. The latest 3D scanning technology is introduced. Common applications are listed along with advantages of high accuracy and disadvantages of potential errors from dirty fingers.
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
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.
This document proposes using a cuckoo search algorithm to optimize the process of fingerprint matching for biometric identification. It begins by introducing biometric recognition and some of its challenges with large and complex datasets. It then provides background on cuckoo search optimization and describes how it can be applied to optimize fingerprint matching. Specifically, it presents an algorithm that extracts sub-matrices of increasing dimension from a fingerprint image matrix and uses cuckoo search to match fingerprints by comparing the sub-matrices until an accurate match is found. The document simulates this algorithm and outlines the results, demonstrating how cuckoo search optimization may help address limitations of traditional techniques for complex biometric analysis.
The document summarizes biometric security and fingerprint recognition technology. It defines biometrics as using physical or behavioral traits like fingerprints, iris, face, voice or handwriting to identify individuals. Fingerprints are widely used for authentication and various fingerprint recognition techniques are described, including minutiae-based matching of ridge endings and bifurcations. Applications of fingerprint biometrics include security systems, criminal identification, and border control. Emerging areas include 3D and multi-view fingerprint capture to overcome limitations of contact sensors.
GANNON UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
FALL2015
GECE 572: DIGITAL SIGNAL PROCESSING
FINGER PRINT RECOGNITION USING MINUTIAE BASED FEATURE
FINAL PROJECT
Prepared by
THADASINA PRUTHVIN REDDY
[email protected]
SALMAN SIDDIQUI
[email protected]
Instructor:
Dr. Ram Sundaram
Table of contents
1. Abstract
2. Introduction
3. Fingerprint matching
4. Pre-processing stage
5. Minutiae extraction stage
6. Post-processing stage
7. Merits & Demerits
8. Applications & future scope
9. Conclusions
10.References
1. Abstract
Nowadays, conventional identification methods such as driver's license, passport, ATM cards and PIN codes do not meet the demands of this wide scale connectivity. Automated biometrics in general, and automated fingerprint authentication in particular, provide efficient solutions to these modern identification problems. Fingerprints have been used for many centuries as a means of identifying people. The fingerprints of individual are unique and are stay unchanged during the life time. Fingerprint matching techniques can be placed into two categories, minutiae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality the correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.
2. Introduction
Biometric recognition refers to the use of distinctive physiological (e.g. fingerprint, palm print, iris, face) and behavioral (e.g. gait, signature) characteristics, called biometric identifiers for recognizing individuals.
Fingerprint recognition is one of the oldest and most reliable biometric used for personal identification. Fingerprint recognition has been used for over 100 years now and has come a long way from tedious manual fingerprint matching. The ancient procedure of matching fingerprints manually was extremely cumbersome and time-consuming and required skilled personnel.
Finger skin is made up of friction ridges and sweat pores all along these ridges. Friction ridges are created during fetal life and only the general shape is genetically defined. The distinguishing nature of physical characteristics of a person is due to both the inherent individual genetic diversity within the human population as well as the random processes affecting the development of the embryo. Friction ridges ...
Iaetsd latent fingerprint recognition and matchingIaetsd Iaetsd
The document discusses latent fingerprint recognition and matching using statistical texture analysis. It proposes extracting three statistical features from fingerprints - entropy coefficient from intensity histogram, correlation coefficient using Wiener filter, and wavelet energy coefficient from 5-level wavelet decomposition. These features are used to represent fingerprints mathematically and provide efficient fingerprint recognition. Existing fingerprint recognition methods are also discussed, including those based on minutiae matching and dealing with nonlinear distortions. However, these do not fully address the problem. The proposed statistical analysis approach can provide more accurate recognition results.
MDD Project Report By Dharmendra singh [Srm University] Ncr DelhiDharmendrasingh417
In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Generation of Skin Diseases into Synthetic FingerprintsCSCJournals
The document describes a proposed algorithm for generating synthetic fingerprints that appear affected by common skin diseases like warts and atopic dermatitis. The algorithm modifies existing synthetic fingerprints generated using the SFinGe tool to simulate the visual effects of these diseases on fingerprint ridge structure and quality. It first analyzes real fingerprints affected by each disease to understand characteristic features. It then introduces methods to localize fingerprints and draw simulated warts or lines/patches onto synthetic fingerprints in a way that decreases their recognition scores and quality metrics when tested using commercial and open-source fingerprint analysis tools. The results confirm the algorithm successfully modifies fingerprints to appear disease-affected.
Infrared Vein Detection System For Person Identification – An Image Processin...IRJET Journal
This document presents a method for identifying individuals using infrared detection of vein patterns in the hands. The proposed system uses a near-infrared camera to capture images of hand veins. It then applies image processing techniques like region of interest extraction, contrast enhancement, edge detection, and feature extraction using Radon transforms to analyze the vein patterns. Features are matched against a database to identify individuals. The system achieved an accuracy of 92% on a test database of 100 individuals. The document describes the full methodology and provides experimental results demonstrating the effectiveness of infrared vein detection for biometric identification applications.
IRJET- Sixth Sense Hand-Mouse Interface using Gestures & Randomized KeyIRJET Journal
The document proposes a sixth sense ATM machine that enables contactless transactions using hand gestures recognized by cameras rather than physical interfaces, eliminating the spread of bacteria while also incorporating sensors to prevent theft and monitor users to increase security. Current ATM systems are susceptible to bacterial spread through physical interfaces and card/PIN skimming, while proposed solutions in previous research had limitations, so the sixth sense ATM aims to address both issues simultaneously through a non-touch gesture-based interface and integrated alarm sensors.
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.
This document summarizes a research paper on implementing a fingerprint-based biometric authentication system for ATMs using a PIC microcontroller. It describes how fingerprint identification works by analyzing ridge and valley patterns. The system uses a PIC16F877A microcontroller to collect fingerprint data from a fingerprint sensor module and match it to an enrolled fingerprint template to authenticate users. If a match is found, the ATM cashbox opens, and if not, an alarm sounds. The document discusses the methodology, advantages, limitations and components of the system, including the fingerprint sensor, microcontroller, LCD display, motor driver, and buzzer.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Finger vein based biometric security systemeSAT Journals
Abstract Finger vein recognition is a kind of biometric authentication system. This is one among many forms of biometrics used to recognize the individuals and to verify their identity. This paper presents a finger vein authentication system using template matching. Implementation using Matlab shows that the finger vein authentication system performs well for user identification. Keywords – biometric, feature extraction, figure vein, security system
Similar to International Journal of Biometrics and Bioinformatics(IJBB) Volume (3) Issue (1) (20)
3. Table of Contents
Volume 3, Issue , Febuary 2009.
Pages
1-9 Reducing Process-Time for Fingerprint Identification System
Chander Kant, Rajender Nath.
10 - 18 Designing an Artificial Neural Network Model for the Prediction of
Thrombo-embolic Stroke
Shanthi Dhanushkodi, G.Sahoo , Saravanan Nallaperumal
International Journal of Biometrics and Bioinformatics, (IJBB), Volume (3) : Issue (1)
4. Chander Kant & Rajender Nath
Reducing Process-Time for Fingerprint Identification System
Chander Kant ckverma@rediffmail.com
Lecturer, Department of Computer Science and
applications K.U.
Kurukshetra, Haryana (INDIA)
Rajender Nath rnath_2k3@rediffmail.com
Reader, Department of Computer Science and
applications K.U.
Kurukshetra, Haryana (INDIA)
ABSTRACT
Fingerprints are the most widely used biometric feature for person identification and
verification in the field of biometric identification. Fingerprints possess two main types of
features that are used for automatic fingerprint identification and verification: (i) Ridge
and furrow structure that forms a special pattern in the central region of the fingerprint
and (ii) Minutiae details associated with the local ridge and furrow structure. In a
traditional biometric recognition system, the biometric template is usually stored on a
central server during enrollment. The candidate biometric template captured by the
biometric device is sent to the server where the processing and matching steps are
performed. This paper presents an approach to speed up the matching process by
classifying the fingerprint pattern into different groups at the time of enrollment, and
improves fingerprint matching while matching the input template with stored template.
To solve the problem, we take several aspects into consideration like classification of
fingerprint, singular points. The algorithm result indicates that this approach manages to
speed up the matching effectively, and therefore prove to be suitable for large database
like forensic divisions.
Keywords: Biometrics, identification, verification, minutiae points, singular points.
1. INTRODUCTION
Fingerprint is one of the most mature biometric traits and considered legitimate proof of evidence in
courts of law all over worldwide. Fingerprints are, therefore, used in forensic divisions worldwide for
criminal investigations. More recently, an increasing number of civilian and commercial applications are
either using or actively considering using fingerprint-based identification because of a better
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 1
5. Chander Kant & Rajender Nath
understanding of fingerprints as well as demonstrated matching performance than any other existing
biometric technology. Modern fingerprint matching techniques were initiated in the late 16th century [1].
Henry Fauld, in 1880, first scientifically suggested the individuality and uniqueness of fingerprints. At the
same time, Herschel asserted that he had practiced fingerprint identification for about 20 years [2]. This
discovery established the foundation of modern fingerprint identification. In the late 19th century, Sir
Francis Galton conducted an extensive study of fingerprints [2]. He introduced the minutiae features for
single fingerprint classification in 1888. The discovery of uniqueness of fingerprints caused an immediate
decline in the prevalent use of anthropometric methods of identification and led to the adoption of
fingerprints as a more efficient method of identification.
FIGURE 1: Fingerprints classification involving six categories: (a) arch, (b) tented arch, (c) right loop, (d) left loop, (e)
whorl, and (f) twin loop. Critical points in a fingerprint, called core and delta, are marked as squares and triangles.
Note that an arch does not have a delta or a core.
An important advance in fingerprint identification was made in 1899 by Edward Henry, who established
the famous “Henry system” of fingerprint classification [1, 2]: an elaborate method of indexing fingerprints
very much tuned to facilitating the human experts performing (manual) fingerprint identification. In the
early 20th century, fingerprint identification was formally accepted as a valid personal identification
method by law enforcement agencies and became a standard procedure in forensics [2]. Fingerprint
identification agencies were setup worldwide and criminal fingerprint databases were established [2].
Loop, whorl, and twin loop.
1.1 Fingerprint Feature Extraction
The human fingerprint is comprised of various types of ridge patterns, traditionally classified according to
the decades-old Henry system: left loop, right loop, arch, whorl, and tented arch.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 2
6. Chander Kant & Rajender Nath
FIGURE 2: Minutiae- points on a fingerprint.
Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3, and perhaps 5-10% are arches [3].
These classifications are relevant in many large-scale forensic applications, but are rarely used in
biometric authentication. Many types of minutiae exist, including dots (very small ridges), islands (ridges
slightly longer than dots, occupying a middle space between two temporarily divergent ridges), ponds or
lakes (empty spaces between two temporarily divergent ridges), spurs (a notch protruding from a ridge),
bridges (small ridges joining two longer adjacent ridges), and crossovers (two ridges which cross each
other) [4].
1.2 Fingerprint-Matching Process
Fingerprint matching techniques can be placed into two categories: minutiae-based and correlation
based. But the commonly used technique with minimum FAR and FRR is Minutiae-based techniques. In
this process we, first find minutiae points and then map their relative placement on the finger. However,
there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately
when the fingerprint is of low quality. Also this method does not take into account the global pattern of
ridges and furrows [5]. Fingerprint Verification System is a system that determines the correspondence of
an input fingerprint with a template fingerprint stored in data base. A typical block diagram of biometric
matching systems is shown in Figure 3.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 3
7. Chander Kant & Rajender Nath
FIGURE 3: Block diagram of a typical Automatic Fingerprint Verification system.
In a verification fingerprint system, the template fingerprint image is obtained in the enrollment phase.
After that verification process takes place by a inputting the sample of the user’s fingerprint at sensor.
Such input fingerprint must be processed, in the preprocessing step. The preprocessing includes image
enhancement, gray level adjust, ridge thinning, etc. After the fingerprint image has been preprocessed,
the feature extraction block extracts the relevant information that will be used for matching with the
template fingerprint [6]. Finally a verification decision is made with the results or percentages of similarity
obtained from the matching step. Section 2 describes the work in this field and the problems associated
with this field. Section 3 describes the proposed work and the efficiency of proposed work based on
experimental calculations.
2. Related Work
Figure3 above shows the process of matching for fingerprint. It is obvious that if fingerprint templates are
stored in a particular manner then it will quite increase the efficiency of biometrics device. We have visited
Madhuban Forensic Laboratory, Karnal to know which methods are used there as there are lots of
templates in database. There we see the matching process of fingerprint identification among apx.
150000 database templates. The software being used there is FACTS (Finger Analysis Criminal Tracing
System) developed by CMC and based on the theory of Dr. Henry Faulds. This approach involves the
print of all the fingers of both hands and assign weights to each of fingers print pattern [7].
2.1 Explanation of the Henry Classification System
The Henry Classification System allows for logical categorization of ten-print fingerprint records into
primary groupings based on fingerprint pattern types. This system reduces the effort necessary to search
large numbers of fingerprint records by classifying fingerprint records according to gross physiological
characteristics.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 4
8. Chander Kant & Rajender Nath
FIGURE 4: Both palm print of a single person.
Subsequent searches (manual or automated) utilizing granular characteristics such as minutiae are
greatly simplified. Henry Classification System assigns each finger a number according to the order in
which is it located in the hand, beginning with the right thumb as number 1 and ending with the left
pinky/little as number 10. The system also assigns a numerical value to fingers that contain a whorl
pattern; fingers 1 and 2 each have a value of 16, fingers 3 and 4 have a value of 8, and so on, with the
final two fingers having a value of 1. Fingers with a non-whorl pattern, such as an arch or loop pattern,
have a value of zero. Images of various fingerprint patterns are shown already in figure1. In accordance
to the Henry Classification System, finger numbers and finger values are assigned as following: The
fingerprint record’s primary grouping is determined by calculating the ratio of one plus the sum of the
values of the whorl-patterned, even-numbered fingers; divided by one plus the sum of the values of the
whorl-patterned, odd-numbered fingers (Harling 1996). This formula is represented below [8]:
Henry Classification System Formula:
Primary Grouping Ratio (PGR) =
1+ (Sum of whorled, EVEN finger value)
1+ (Sum of whorled, ODD finger value)
If, for example, an individual has a fingerprint record with a LWAALALWLA pattern series (the series
begins with Finger 1, the right thumb and ending with Finger 10, the left pinky), the corresponding
classification ratio would be 19:1. This example is calculated below [8] :
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 5
9. Chander Kant & Rajender Nath
Therefore, this individual belongs to the 19:1 primary group. If, for example, an individual does not have
any whorl-patterned fingerprints, his or her classification ratio, or primary group, would be 1:1. If an
individual has all ten fingerprints containing a whorl pattern, his or her classification ratio would be 31:31.
The Henry Classification System allows for up to 1,024 primary groupings.
2.2 Problems associated with Existing System
Above method work very efficiently when we have palm prints of all fingers of both hands. We assign
weights to the person prints and calculate PGR. On the basis on PGR factor the search goes to particular
domain and identified the proper match. But if we have only one finger print as input print, then there will
be problem as in this case we can’t find PGR factor. Further the problem can also arise if the criminal is
made some trick while giving its input prints to the system. He can change the order of his fingerprint
while giving input print, if this happen then his print can’t be matched anywhere in the system.
3. PROPOSED WORK
Proposed work is based on the theory of fingerprint classification, we store only single finger print of
person in the database. This single print can be thumb print or print of index finger. One obvious
advantage of this approach is that it will considerably reduce the amount of memory required to store the
fingerprint template as only one print is stored instead of 10 prints for an individual. Now let us see how
the proposed system will work. First let us talk about the enrollment process; in conventional system the
database contains the fingerprint templates in an ordinary manner. But here in our proposed system the
database contains the different set of templates according to classification. During the enrollment
process, sensor senses the fingerprint, then next step is feature extraction, here minutiae points are
extracted. After this step we put a classifier to check the classification of input template that whether it is
left-loop, right-loop, arch or whorl. The detail function of classifier is shown in figure 6. After classification
the input template will be stored in particular domain. A domain in the database contains the templates of
same classification. Normally the fingerprints are classified as Whorl, arch and loop. Loops make up
nearly 65% of all fingerprints, whorls are nearly 30%, and perhaps 5% are arches [3]. These
classifications are relevant in many large-scale forensic applications, but are rarely used in biometric
authentication. Since the loops are 65%, we further divide this domain into two parts i.e. left loop 32%
apx. and right loop 33% apx . So we have four different domains i) Left-Loop ii) Right-Loop iii) Arch and
iv) Whorl as shown in figure5. Now let us come to the verification process, here the finger or finger print is
placed at sensor and then its features are extracted and a final template is generated for matching. Now
this template will not matched with every templates in the database rather it extracts its classified domain
out of 4-domain and will perform match from this extracted domain. This process, no doubt will be fast
and more efficient especially when the stored database is very large that is more than 1, 00000
templates. Let D and T be the representation of the Database Template and Stored Template
respectively. Each minutia may be described by a number of attributes, including its location in the
fingerprint image, orientation, type etc. Most common minutiae matching algorithms consider each
minutiae as a triplet m= {x,y,θ}that indicates the minutiae location coordinates and the minutiae angle θ.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 6
10. Chander Kant & Rajender Nath
D= {m1,m2,…….mn} mi = {xi,yi,θi} i= 1….m
T= { m’1,m’2,…….m’n } mj = {x’j,y’j,θ’j} j= 1….n
Where m and n denotes the number of minutiae in D and T respectively.
Database Template and Stored Template and stored template will be matched, If we calculate Spatial
Distance (SD) and direction difference (DD) that will not be below than specified value r0 and θ0 or we
can write as [9].
SD (m’1, m1) = sqrt [(x’i - xi )2 + (y’i - yi )2 ] >= r0 ----------------(1)
Similarly
DD (m’1, m1) >= θ0 ----------------(2)
FIGURE 5: Proposed scheme for Fingerprint Identification
3.1 Fingerprint Classifier
Fingerprint classifiers classify the input fingerprint into four major categories namely Left-Loop, Right-
Loop, Whorl and Arch. The proposed classifiers works on the basis of singular point (Delta) extracted. If
there are two deltas then it will be counted as whorl or twin loop. If there is no delta then it will be counted
at arch. If only one delta is there then it will be either left loop or right loop.
Whorl Arch Right-Loop Left-Loop
Figures 6. Position and numbers of deltas in different finger prints.
We further find the category of loop by measuring Relative position (R). If relative position, R of delta with
respect to symmetry axis is R = 1 means the delta is on the right side of symmetry axis then it will be left
loop otherwise it will be right loop [10]. On the basis of above idea, a flowchart (figure 6) for algorithm is
designed to find the fingerprint classification.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 7
11. Chander Kant & Rajender Nath
FIGURE 6: Proposed working of Fingerprint classifier.
3.3 Performance Estimation of Proposed Scheme
Let us take the example of Madhuban forensic laboratory, Karnal where database of more than 1, 50000
templates are stored. We had performed an experiment at Madhubhan by inputting a single template at
the sensor and started to identify it from their database. The process takes 25-30 minutes to identify and
also gives 34 matched templates (equations 1, 2 satisfy for 34 templates) [7]. These 34 templates again
have to match manually and consume around 5-6 hours i.e. it’s a quite time consuming and complex task.
First let us see why the system takes so much time.
Let’s assume that time taken to perform a single match = 1 ms (1 milli seconds)
Performance of Existing System
For Best case i.e. the template is First match, Time required = 1 X 1 = 1 ms
For worst case i.e. the template is last match, Time required = 1 X 1, 50000 = 150 sec. = 25 min.
For an Average case, Time required= apx 10-20 min.
Performance of Proposed System
For Best case i.e. the template is First match, Time required = 1 X 1 = 1 ms
Now let us calculate for worst case
We have assumed 1, 50000 templates, According to classification there will be
45000 whorls (30%) + 48000 Left Loop (32%) + 49500 Right Loop (33%) + 7500 Arch (5%)
At First stage we get the template classification and accordingly particular domain will be extracted. Now
we calculate the time taken for each classification
For Whorl = 1ms X 45000 = 45 sec.
For LL = 1ms X 48000 = 48 sec.
For RL = 1ms X 49500= 49.5 sec
For Arch = 1msX 7500= 7.5 sec.
International Journals of Biometric and Bioinformatics, Volume (3) : Issue (1) 8
13. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
Designing an Artificial Neural Network Model for the Prediction of
Thrombo-embolic Stroke
D.Shanthi dshan71@gmail.com
Department of Computer Science and Engineering
Birla Institute of Technology
Budaiya, P.O.Box 31320, Kingdom of Bahrain
Dr.G.Sahoo gsahoo@bitmesra.ac.in
Department of Information Technology
Birla Institute of Technology
Mesra, Ranchi, India
Dr.N.Saravanan saranshanu@gmail.com
Department of Computer Science and Engineering
Birla Institute of Technology
Budaiya, P.O.Box 31320, Kingdom of Bahrain
ABSTRACT
In this study, a functional model of ANN is proposed to aid existing diagnosis
methods. This work investigated the use of Artificial Neural Networks (ANN) in
predicting the Thrombo-embolic stroke disease. The Backpropogation algorithm
was used to train the ANN architecture and the same has been tested for the
various categories of stroke disease. This research work demonstrates that the
ANN based prediction of stroke disease improves the diagnosis accuracy with
higher consistency. This ANN exhibits good performance in the prediction of
stroke disease in general.
Keywords: Artificial Intelligence, BPN, Neural Network, Thrombo-embolic Stroke.
1. INTRODUCTION
Neural networks provide a very general way of approaching problems. When the output of the
network is continuous, it is performing prediction and when the output has discrete values, then it
is doing classification. A simple rearrangement of the neurons and the network becomes adept at
detecting clusters. Computer Assisted Decision Support in medicine has at least the role of
enhancing the consistency of care. Secondly, it has the potential to cover rare conditions, since
no clinical expert can be expected to possess encyclopedic knowledge of all of the exceptional
manifestations of diseases, even within a specialist domain. Thirdly, the expanding range of
patient information that is made available in electronic form, makes it feasible to more accurately
quantify important clinical indicators, such as the relative likelihood for competing diagnoses or
the clinical outcome. In some cases, computer-assisted diagnoses have been claimed to be
even more accurate than those by clinicians.
Stroke is a life-threatening event in which part of the brain is not getting enough oxygen. There
are different types of stroke namely Brain Attack, Embolic Stroke, Thrombotic Stroke, Ischemic
Stroke, Cerebrovascular Accident (CVA). Medical personnel treating a stroke are challenged to
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14. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
treat the patient as quickly as possible to avoid permanent tissue damage or death. Strokes were
responsible for more deaths and nearly half of those deaths occurred outside of a hospital.
Stroke is the third leading cause of death, behind heart disease and cancer. Most recovery
occurs during the first few months following a stroke. According to the National Institute of Health,
the risk of stroke is greater – and the recovery process is slower. Thrombo embolic strokes are
caused by fatty deposits (plaques) that have built up in the arteries carrying blood to the brain.
This slows blood flow and can cause clots to form on the plaques that narrow or block the flow of
oxygen and nutrients to the brain. It is also caused by a blood clot formed in another part of the
body that breaks loose, travels through the bloodstream, and blocks an artery carrying oxygen
and nutrients to the brain. When travelling through the body the blood clot is called an embolus
[1]. A hemorrhagic stroke is caused when an artery supplying blood bleeds into the brain. The
broken blood vessel prevents needed oxygen and nutrients from reaching brain cells. One type of
hemorrhagic stroke is caused when an artery that has weakened over time bulges (called an
aneurysm) and suddenly bursts [2]. Thrombo-embolic Stroke can be classified as Transient
Ischemic attacks (TIA), Evolving Stroke, Completed Stroke, Residual Squeal, Classical Stroke,
Inappropriate Stroke, Anterior Cerebral Territory Stroke, Posterior Cerebral Stroke, Middle
Cerebral Territory Stroke. Hemorrhagic Stroke can be classified as Cerebellar stroke, Thalamic
Stroke and Cortical Stroke. In this paper, we propose an artificial neural network model for the
prediction of Thrombo Embolic Stroke disease. The rest of the paper discusses about related
studies, the proposed model, results and discussion along with conclusion.
2. RELATED STUDIES OF ANN IN MEDICINE
ANNs appear to be a valid candidate for the reliability analysis. Given a number of predictor
variables, an opportunely structured and trained Multi Layer Perceptron (MLP) can identify the
“causal path” leading to a certain value of the potential objective variables, with a certain degree
of confidence. [3]. Several studies have applied neural networks in the diagnosis of
cardiovascular disease, primarily in the detection and classification of at-risk people from their
ECG waveforms [4]. In the works of [5], the application of neural networks to classify normal and
abnormal (pathological) ECG waveforms and the abnormal ECG recordings had six different
disease conditions. The classifier was able to recognize these waveforms with 70.9% accuracy.
The Study [6] suggested that the role of the ANN, which uses non-linear statistics for pattern
recognition in predicting one-year liver disease-related mortality using information available
during initial clinical evaluation. MLP with sigmoidal feed-forward and standard Back-Propagation
(BP) learning algorithm was employed as a forecaster for bacteria-antibiotic interactions of
infectious diseases. Comparing ANN ensembles with logistic regression models we found the
former approach to be better in terms of ROC area and calibration assessments. Both ANN and
logistic regression models showed intra-method variations, as a result of training the models with
different parts of the study population. This variation was larger for the ANN ensemble models [7].
The studies of application of ARTMAP in medicine include classification of cardiac arrhythmias [8]
and treatment selection for schizophrenic and unipolar depressed in-patients [9]. Another study
revealed that fully connected feed forward MLP and BP learning rule, were able to predict
patients with colorectal cancer more accurately than clinicopathological methods. They indicate
that NN predict the patients’ survival and death very well compared to the surgeons. The study
[10] presents their study for the diagnosis of Acute Myocardial Infarction. The results show that
NN performance is 0.84 and 0.85 under ROC.
A neural network can provide a considerable improvement in the diagnosis of early acute allograft
rejection, though further development work is needed before this becomes a routine diagnostic
tool. The selection of cases used to train the network is crucial to the quality of its performance.
There is scope to improve the system further by incorporating clinical information[11]. Another
methodology, which is based on ANNs, has been developed for the detection of ischaemic
episodes in long-duration ECG recordings [12] . The raw ECG signal containing the ST segment
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15. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
and the T wave of each beat is the input to the beat-classification system, and the output is the
classification of the beat. The use of the ANN model as a data mining tool is very promising for
new knowledge discovery in nephrology, to model complex behaviour of different molecular
markers of dialysis treatment and for online treatment monitoring. [13]. The Study [14],
presented a fully automated method using ANNs were compared with the clinical interpretation.
The neural networks trained with both perfusion and ECG-gated images had a 4–7% higher
specificity compared with the corresponding networks using perfusion data only, in four of five
segments compared at the same level of sensitivity. The addition of functional information from
ECG-gated MPS is of value for the diagnosis of myocardial infarction using an automated method
of interpreting myocardial perfusion images.
3. THE PROPOSED MODEL
3.1 Patient Data
The data for this study have been collected from 50 patients who have symptoms of stroke
disease. The data have been standardized so as to be error free in nature. All the fifty cases are
analyzed after careful scrutiny with the help of the Physicians. Table-1 below shows the various
input parameters for the prediction of stroke disease.
Sl.No. Parameters
1 Age
2 Sex
3 Pre-stroke mobility
4 Hypertension
5 Diabetes Mellitus
6 Myocardial infarction
7 Cardiac failure
8 Atrial fibrillation
9 Smoking
10 High blood cholesterol
11 Alcohol abuse
12 Weakness of Left Arm and Left leg
13 Weakness of Right Arm and Right leg
14 Slurring of Speech
15 Giddiness
16 Headache
17 Vomiting
18 Memory Deficits
19 Swallowing Difficulties
20 Loss of Vision
21 Isolated vertigo
22 Transient Double Vision
23 Sudden difficulty in walking , dizziness or loss
of balance
24 Hand / Leg numbness
25 Transient loss of consciousness
TABLE 1: Input Parameters for Prediction of Stroke
3.2 Feature Selection
Data are analyzed in the dataset to define column parameters and data anomalies. Data analysis
information needed for correct data preprocessing. After data analysis, the values have been
identified as missing, wrong type values or outliers and which columns were rejected as
unconvertible for use with the neural network[15]. Feature selection methods are used to identify
input columns that are not useful and do not contribute significantly to the performance of neural
network. In this study, Backward stepwise method is used for input feature selection. The
removal of insignificant inputs will improve the generalization performance of a neural network.
This method begins with all inputs and it works by removing one input at each step. At each step,
the algorithm finds an input that least deteriorates the network performance and becomes the
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16. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
candidate for removal from the input set. Table 2 shows the finalized input parameters after
applying feature selection method.
Input column name Code Importance %
Hypertensive X1 1.626103
Diabetes X2 4.229285
Myocardial X3 0.043249
Cardiac failure X4 0.001659
Atrial fibrillation X5 0.034991
Smoking X6 2.061142
Blood cholesterol X7 8.831646
left arm&leg X8 19.209636
Right arm &leg X9 2.832501
Slurring X10 1.776497
Giddiness X11 3.64891
Headache X12 15.646755
Vomiting X13 0.535259
memory deficits X14 1.485701
Swallowing X15 5.224413
Vision X16 7.366008
Double vision X17 1.13867
Vertigo X18 15.471004
Numbness X19 0.136173
Dizziness X20 8.700397
TABLE 2: Percentage of Importance of Input Data
3.3 Neural Network Architecture
The architecture of the neural network used in this study is the multilayered feed-forward network
architecture with 20 input nodes, 10 hidden nodes, and 10 output nodes. The number of input
nodes are determined by the finalized data; the number of hidden nodes are determined through
trial and error; and the number of output nodes are represented as a range showing the disease
classification. The most widely used neural-network learning method is the BP algorithm [16].
Learning in a neural network involves modifying the weights and biases of the network in order to
minimize a cost function. The cost function always includes an error term a measure of how
close the network's predictions are to the class labels for the examples in the training set.
Additionally, it may include a complexity term that reacts a prior distribution over the values that
the parameters can take.
The activation function considered for each node in the network is the binary sigmoidal function
-x
defined (with σ = 1) as output = 1/(1+e ), where x is the sum of the weighted inputs to that
particular node. This is a common function used in many BPN. This function limits the output of
all nodes in the network to be between 0 and 1. Note all neural networks are basically trained
until the error for each training iteration stopped decreasing. Figure 1 shows the architecture of
the specialized network for the prediction of stroke disease. The complete set of final data (20
inputs) are presented to the generic network, in which the final diagnosis corresponds to output
units.
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17. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
FIGURE 1: Artificial Neural Network Architecture for the prediction of stroke disease
The net inputs and outputs of the j hidden layer neurons can be calculated as follows
N +1
net h = ∑ W ji xi
j
t =1
y j = f (net h )
j
Calculate the net inputs and outputs of the k output layer neurons are
J +1
netko = ∑ Vkj y j
j =1
Z k = f (netko )
Update the weights in the output layer (for all k, j pairs)
vkj ← vkj + cλ ( d k − Z k ) Z k (1 − Z k ) y j
Update the weights in the hidden layer (for all i, j pairs)
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18. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
k
w ji ← w ji + cλ 2 y j (1 − y j ) xi (∑ (d k − zk ) zk (1 − Z k )vkj )
k =1
Update the error term
k
E ← E + ∑ (d k − zk )2
k =1
and repeat from Step 1 until all input patterns have been presented (one epoch). If E is below
some predefined tolerance level , then Stop. Otherwise, reset E = 0, and repeat from Step 1 for
another epoch.
The inputs to the models were 20 variable training parameters and the output indicated the
point at which training should stop. The following are the results generated from the input given
to the neural network after going through the process of careful training, validation and testing
using Neuro Intelligence tool. Table 3 shows the various categories of Stroke diseases and their
classification.
Output Code
TIA D1
Left Hemiplegia D2
Right Hemiplegia D3
Dysphasia D4
Monoplegia D5
Left Hemianopia D6
Aphasia D7
Right Hemianesthesia D8
Dysphagia D9
Quadruplegia D10
TABLE 3: Output Classification
4. RESULTS AND DISCUSSION
The Data have been analyzed using Neuro-intelligence tool [17].During analysis, the
column type is identified. During data analysis, the last column is considered as the target one
and other columns will be considered as input columns. The dataset is divided in to training set,
validation set and test set.
Sl.No Data Partition set Records Percentage
1. Training set 34 68%
2. Validation set 8 16%
3. Test set 8 16%
4. Ignored set 0 0%
Total 50 100%
TABLE 4: Data Partition Set
Training a neural network is the process of setting the best weights on the inputs of each of the
units. The goal is to use the training set to produce weights where the output of the network is as
close to the desired output as possible for as many of the examples in the training set as
possible. Also it has been proved that Genetic Algorithm and Back-Propagation neural network
hybrids in selecting the input features for the neural network reveals the performance of ANN can
be improved by selecting good combination of input variables [18]. The training set is a part of
the input dataset used for neural network training, i.e. for adjustment of network weights. The
validation set is a part of the data are used to tune network topology or network parameters other
than weights. For example, it is used to define the number of units of to detect the moment when
the neural network performance started hidden to deteriorate. To choose the best network (i.e. by
changing the number of units in the hidden layer) the validation set is used. The test set is a part
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19. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
of the input data set used to test how well the neural network will perform on new data. The test
set is used after the network is ready (trained), to test what errors will occur during future network
application. This set is not used during training and thus can be considered as consisting of new
data entered by the user for the neural network application.
Figure 2 shows the various data set errors with respect to training set, validation set and the best
network. After training through repeated iterations it reaches the level of best network.
FIGURE 2: Data Set Errors
In the diagnosis of stroke , it is not always possible to make a clear-cut determination of disease,
because of variability in the diagnostic criteria, age at onset, and differential presentation of
disease. Mapping such diseases is greatly simplified if the data present a homogeneous genetic
trait and if disease status can be reliably determined. Here, we present an approach to
determination of disease status, using methods of artificial neural-network analysis. The Network
errors have been shown graphically in figure 3. After 150 iterations, the network error has been
decreased and from 300 iterations it is almost 0.
FIGURE 3: Network Errors vs. Training Error
The trained network has been tested with a test set, in which the outcomes are known but not
provided to the network, to see how well the training has worked.. We used diagnostic criteria and
disease status to train a neural network to classify individuals as "affected" by several categories
of stroke as given below .
The analysis shows clearly that 32% of the respondents have the symptoms of Left Hemiplegia;
14% each have the symptoms of TIA and Right Hemiplegia respectively; 10% of the patients
have the symptoms of Dysphasia and 6% are suffering from Monoplegia. 8% each have the
symptoms of Left Hemianopia and Aphasia. In the meantime, 4% have the symptoms of Right
Hemianesthesia. 2% each have the symptoms of Dysphagia and Quadruplegia respectively and
is diagrammatically depicted in figure 4.
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20. D.Shanthi, Dr.G.Sahoo & Dr.N.Saravanan
Disease Classification
40
No of Cases(%)
30
20 %
10
0
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10
% 14 32 14 10 6 8 8 4 2 2
Diseases
FIGURE 4: Various Stroke Diseases vs. Number of Cases
5. CONCLUSION
Neural networks have been proposed as useful tools in decision making in a variety of medical
applications. Neural networks will never replace human experts but they can help in screening
and can be used by experts to double-check their diagnosis. In general, results of disease
classification or prediction task are true only with a certain probability. This work described here
shows that the prediction of risk from stroke gives best results on the dataset used. The results
generated by this system have been verified with the physicians and are found correct. This ANN
model helps the doctors to plan for a better medication and provide the patient with early
diagnosis as it performs reasonably well even without retraining. In conclusion, when the ANN
was trained and tested after optimizing the input parameters, the overall predictive accuracy
obtained was 89%.
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