This document summarizes a research paper that proposes an image preprocessing algorithm to improve the quality of digital fingerprint images for recognition purposes. The algorithm includes steps like grayscale conversion, image normalization, segmentation to remove noisy areas, estimation of directional and frequency maps to determine fingerprint ridge orientation and frequency. The goal is to enhance image quality before feature extraction and matching, which can improve recognition performance by reducing errors and processing time.
Portable and Efficient Fingerprint Authentication System Based on a Microcont...IJECEIAES
This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.
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
Ingerprint based student attendance system with sms alert to parentseSAT Journals
Abstract
This paper is a study of a fingerprint recognition system based on minutiae based fingerprint algorithms used in various techniques. This line of track mainly involves extraction of minutiae points from the model fingerprint images and fingerprint matching based on the number of minutiae pairings among two fingerprints. This paper also provides the design method of fingerprint based student attendance with help of GSM. This system ignores the requirement for stationary materials and personnel for keeping of records.
Keywords – GSM, LCD
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
Attendance Monitoring System of Students Based on Biometric and GPS Tracking ...IJAEMSJORNAL
This paper is a study of a fingerprint recognition system based on minutiae based fingerprint algorithms used in various techniques. This line of track mainly involves extraction of minutiae points from the model fingerprint images and fingerprint matching based on the number of minutiae pairings among to fingerprints. This paper also provides the design method of fingerprint based student attendance with help of GSM. This system ignores the requirement for stationary materials and personnel for keeping of records. The main objective of this project is to develop an embedded system, which is used for security applications. The biometrics technology is rapidly progressing and offers attractive opportunities. In recent years, biometric authentication has grown in popularity as a means of personal identification in college administration systems. The prominent biometric methods that may be used for authentication include fingerprint, palmprint, and handprint, face recognition, speech recognition, dental and eye biometrics. In this paper, a microcontroller based prototype of attendance system using fingerprint sensor and face recognition module is implemented. The tracking module is used here to identify the location of the missing person.
Design and development of sapling monitoring systemIJCSEA Journal
The measurement of a plant growth without interrupting its natural growth is essential since its diameter and height are related to the development of a tree as well as the water contents. Therefore main objective of this project is to find the diameter and height of a plant. Hence in this proposed work, an interface between a C3008 smart camera and a computer has been developed. Main advantage of this camera module is that it has digital output and digital video port that supplies a continuous 8-16 bit range of data stream. It is used to capture the image of a plant at different intervals of time. A MATLAB GUI has been developed for performing Sobel Edge Detection on a plant image to determine its height, maximum and minimum width. Such programs enable us to automatically measure and record the various parameters of a plant. The results attained prove that the system is capable of measuring the changes (diameter and height changes) of plant’s growth accurately.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised...CSCJournals
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
Portable and Efficient Fingerprint Authentication System Based on a Microcont...IJECEIAES
This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.
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
Ingerprint based student attendance system with sms alert to parentseSAT Journals
Abstract
This paper is a study of a fingerprint recognition system based on minutiae based fingerprint algorithms used in various techniques. This line of track mainly involves extraction of minutiae points from the model fingerprint images and fingerprint matching based on the number of minutiae pairings among two fingerprints. This paper also provides the design method of fingerprint based student attendance with help of GSM. This system ignores the requirement for stationary materials and personnel for keeping of records.
Keywords – GSM, LCD
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
Attendance Monitoring System of Students Based on Biometric and GPS Tracking ...IJAEMSJORNAL
This paper is a study of a fingerprint recognition system based on minutiae based fingerprint algorithms used in various techniques. This line of track mainly involves extraction of minutiae points from the model fingerprint images and fingerprint matching based on the number of minutiae pairings among to fingerprints. This paper also provides the design method of fingerprint based student attendance with help of GSM. This system ignores the requirement for stationary materials and personnel for keeping of records. The main objective of this project is to develop an embedded system, which is used for security applications. The biometrics technology is rapidly progressing and offers attractive opportunities. In recent years, biometric authentication has grown in popularity as a means of personal identification in college administration systems. The prominent biometric methods that may be used for authentication include fingerprint, palmprint, and handprint, face recognition, speech recognition, dental and eye biometrics. In this paper, a microcontroller based prototype of attendance system using fingerprint sensor and face recognition module is implemented. The tracking module is used here to identify the location of the missing person.
Design and development of sapling monitoring systemIJCSEA Journal
The measurement of a plant growth without interrupting its natural growth is essential since its diameter and height are related to the development of a tree as well as the water contents. Therefore main objective of this project is to find the diameter and height of a plant. Hence in this proposed work, an interface between a C3008 smart camera and a computer has been developed. Main advantage of this camera module is that it has digital output and digital video port that supplies a continuous 8-16 bit range of data stream. It is used to capture the image of a plant at different intervals of time. A MATLAB GUI has been developed for performing Sobel Edge Detection on a plant image to determine its height, maximum and minimum width. Such programs enable us to automatically measure and record the various parameters of a plant. The results attained prove that the system is capable of measuring the changes (diameter and height changes) of plant’s growth accurately.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised...CSCJournals
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
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.
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.
Developmentof Image Enhancement and the Feature Extraction Techniques on Rura...IOSR Journals
Abstract: Fingerprint recognition is one of the most popular and successful methods used for person
identification which takes advantage of the fact that the fingerprint has some unique characteristics called
minutiae which are points where a extracts the ridges and bifurcation from a fingerprint image. A critical step in
studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images.
However fingerprint images are rarely of perfect quality. Fingerprint image enhancement techniques are
employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations.
Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various
distortions introduced during the acquisition process. In this paper we have used the rural fingerprints
database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images. Out of
which we have preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint
image and tried to improve the quality of images. The Resultant images quality is verified by using different
quality measures.
Keywords: minutiae extraction, extracts the ridges and bifurcation, rural fingerprint authentication.
Advanced Authentication Scheme using Multimodal Biometric SchemeEditor IJCATR
Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today‟s biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.
Pre-Processing Image Algorithm for Fingerprint Recognition and its Implementa...ijseajournal
Fingerprint recognition technology is becoming increasingly popular and widely used for many applications that require a high level of security. We can meet several types of sensors integrated in the fingerprint recognition system as well as several types of image processing algorithm in order to ensure
reliable and fast authentication of people. Embedded systems have a wide variety and the choice of a welldesigned
processor is one of the most important factors that directly affect the overall performance of the system. This paper introduces a preliminary treatment to the image in order to improve the quality, and then present a hardware implementation.
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.
Using image stitching and image steganography security can be provided to any image which has to be
sent over the network or transferred using any electronic mode. There is a message and a secret image that
has to be sent. The secret image is divided into parts.The first phase is the Encrypting Phase, which deals
with the process of converting the actual secret message into ciphertext using the AES algorithm. In the
second phase which is the Embedding Phase, the cipher text is embedded into any part of the secret image
that is to be sent. Third phase is the Hiding Phase, where steganography is performed on the output image
of Embedding Phase and other parts of the image where the parts are camouflaged by another image using
least significant bit replacement. These individual parts are sent to the concerned receiver. At the
receivers end decryption of Hiding phase and Embedding Phase takes place respectively. The parts
obtained are stitched together using k nearest method. Using SIFT features the quality of the image is
improved.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
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.
A Review on Overview of Image Processing Techniquesijtsrd
Image processing is actually among the fast growing innovations across various areas of a business with applications. Image processing frequently forms key scientific areas within the areas of electronics and computer science. Image processing is a tool for refining raw photographs obtained in our everyday lives from rockets, ships, space samples or military identification flights. Thanks to technologically powerful personal computers, broad databases of current devices and the Graphic Technology and the accessible resources for such software and apps, this area is strong and common. The provided input is an image and its output an enhanced high quality image according to the techniques used in the image processing procedure. Image processing is typically called digital image processing, although it is often possible to optically process and analogy photograph. An overview of image processing methods is given in this article. This article focuses mainly on identifying specific methods utilized in various image processing phases. Hirdesh Chack | Vijay Kumar Kalakar | Syed Tariq Ali "A Review on Overview of Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31819.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/31819/a-review-on-overview-of-image-processing-techniques/hirdesh-chack
Overlapped Fingerprint Separation for Fingerprint AuthenticationIJERA Editor
Overlapped fingerprints captured at the crime scene plays significant role as an evidence to capture the criminals. As latent fingerprints are the accidently left skin impressions, so these are found to be with broken ridge composition, overlapped patterns and spoiled minutiae information. The Graphical User Interface (GUI) system is developed by using MATLAB R2015a software. This project also includes the development of standalone program for this system. The main purpose of GUI development is to get the value of real end points and real-branch points of a overlapped fingerprint image. The value of this point is used in fingerprint image matching process to identify the owner of an overlapped fingerprint image. The image enhancement consists of several process such as histogram equalization process, enhancement by Fast Fourier Transform (FFT) factor, and image binarization while minutiae extraction consist of ridge thinning process, region of interest (ROI) extraction, and minutiae extraction process. All processes should be done one by one.
License plate recognition for toll payment applicationeSAT Journals
Abstract Automatic License Plate Recognition (ALPR) is the method for the extraction of vehicle license plate information from images. It can be used on various applications such as Pay-Per -Use roads (Electronic Toll Collection), Parking lots and arterial traffic conditions monitoring. Automatic License Plate Recognition uses infrared cameras to capture images under varied lighting and weather conditions. The objective of this paper is to implement K-Means Clustering Algorithm for License plate extraction & Maximally stable extreme region for license plate segmentation , Template matching method for license plate recognition & also payment in toll plaza and parking lots automatically by detecting the number plates of vehicles which in turn reduce the traffic and consumption of time in toll stations. Keywords: Automatic License Plate Recognition (ALPR), Maximally Stable Extreme Region (MSER), Template matching, and Character Recognition
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
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
An Enhanced Authentication System Using Face and Fingerprint Technologiesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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.
Developmentof Image Enhancement and the Feature Extraction Techniques on Rura...IOSR Journals
Abstract: Fingerprint recognition is one of the most popular and successful methods used for person
identification which takes advantage of the fact that the fingerprint has some unique characteristics called
minutiae which are points where a extracts the ridges and bifurcation from a fingerprint image. A critical step in
studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images.
However fingerprint images are rarely of perfect quality. Fingerprint image enhancement techniques are
employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations.
Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various
distortions introduced during the acquisition process. In this paper we have used the rural fingerprints
database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images. Out of
which we have preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint
image and tried to improve the quality of images. The Resultant images quality is verified by using different
quality measures.
Keywords: minutiae extraction, extracts the ridges and bifurcation, rural fingerprint authentication.
Advanced Authentication Scheme using Multimodal Biometric SchemeEditor IJCATR
Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today‟s biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.
Pre-Processing Image Algorithm for Fingerprint Recognition and its Implementa...ijseajournal
Fingerprint recognition technology is becoming increasingly popular and widely used for many applications that require a high level of security. We can meet several types of sensors integrated in the fingerprint recognition system as well as several types of image processing algorithm in order to ensure
reliable and fast authentication of people. Embedded systems have a wide variety and the choice of a welldesigned
processor is one of the most important factors that directly affect the overall performance of the system. This paper introduces a preliminary treatment to the image in order to improve the quality, and then present a hardware implementation.
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.
Using image stitching and image steganography security can be provided to any image which has to be
sent over the network or transferred using any electronic mode. There is a message and a secret image that
has to be sent. The secret image is divided into parts.The first phase is the Encrypting Phase, which deals
with the process of converting the actual secret message into ciphertext using the AES algorithm. In the
second phase which is the Embedding Phase, the cipher text is embedded into any part of the secret image
that is to be sent. Third phase is the Hiding Phase, where steganography is performed on the output image
of Embedding Phase and other parts of the image where the parts are camouflaged by another image using
least significant bit replacement. These individual parts are sent to the concerned receiver. At the
receivers end decryption of Hiding phase and Embedding Phase takes place respectively. The parts
obtained are stitched together using k nearest method. Using SIFT features the quality of the image is
improved.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
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.
A Review on Overview of Image Processing Techniquesijtsrd
Image processing is actually among the fast growing innovations across various areas of a business with applications. Image processing frequently forms key scientific areas within the areas of electronics and computer science. Image processing is a tool for refining raw photographs obtained in our everyday lives from rockets, ships, space samples or military identification flights. Thanks to technologically powerful personal computers, broad databases of current devices and the Graphic Technology and the accessible resources for such software and apps, this area is strong and common. The provided input is an image and its output an enhanced high quality image according to the techniques used in the image processing procedure. Image processing is typically called digital image processing, although it is often possible to optically process and analogy photograph. An overview of image processing methods is given in this article. This article focuses mainly on identifying specific methods utilized in various image processing phases. Hirdesh Chack | Vijay Kumar Kalakar | Syed Tariq Ali "A Review on Overview of Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31819.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/31819/a-review-on-overview-of-image-processing-techniques/hirdesh-chack
Overlapped Fingerprint Separation for Fingerprint AuthenticationIJERA Editor
Overlapped fingerprints captured at the crime scene plays significant role as an evidence to capture the criminals. As latent fingerprints are the accidently left skin impressions, so these are found to be with broken ridge composition, overlapped patterns and spoiled minutiae information. The Graphical User Interface (GUI) system is developed by using MATLAB R2015a software. This project also includes the development of standalone program for this system. The main purpose of GUI development is to get the value of real end points and real-branch points of a overlapped fingerprint image. The value of this point is used in fingerprint image matching process to identify the owner of an overlapped fingerprint image. The image enhancement consists of several process such as histogram equalization process, enhancement by Fast Fourier Transform (FFT) factor, and image binarization while minutiae extraction consist of ridge thinning process, region of interest (ROI) extraction, and minutiae extraction process. All processes should be done one by one.
License plate recognition for toll payment applicationeSAT Journals
Abstract Automatic License Plate Recognition (ALPR) is the method for the extraction of vehicle license plate information from images. It can be used on various applications such as Pay-Per -Use roads (Electronic Toll Collection), Parking lots and arterial traffic conditions monitoring. Automatic License Plate Recognition uses infrared cameras to capture images under varied lighting and weather conditions. The objective of this paper is to implement K-Means Clustering Algorithm for License plate extraction & Maximally stable extreme region for license plate segmentation , Template matching method for license plate recognition & also payment in toll plaza and parking lots automatically by detecting the number plates of vehicles which in turn reduce the traffic and consumption of time in toll stations. Keywords: Automatic License Plate Recognition (ALPR), Maximally Stable Extreme Region (MSER), Template matching, and Character Recognition
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
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
Biometrics is one of the most used technologies in the field of security due to its reliability and performance. It is based on several physical human characteristics but the most used technology is the fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in each fingerprint we propose in this article an image preprocessing procedure in order to improve its quality before extracting the necessary information for the comparison phase.
An Enhanced Authentication System Using Face and Fingerprint Technologiesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Biometric Template Protection With Robust Semi – Blind Watermarking Using Ima...CSCJournals
This paper addresses a biometric watermarking technology sturdy towards image manipulations, like JPEG compression, image filtering, and additive noise. Application scenarios include information transmission between client and server, maintaining e-database and management of signatures through insecure distribution channels. Steps involved in this work are, a) generation of binary signature code for biometric, b) embedding of the binary signature to the host image using intrinsic local property, that ensures signature protection, c) host image is then made exposed to various attacks and d) signature is extracted and matched based on an empirical threshold to verify the robustness of proposed embedding method. Embedding relies on binary signature manipulating the lower order AC coefficients of Discrete Cosine Transformed sub-blocks of host image. In the prediction phase, DC values of the nearest neighbor DCT blocks is utilized to predict the AC coefficients of centre block. Surrounding DC values of a DCT blocks are adaptively weighed for AC coefficients prediction. Linear programming is used to calculate the weights with respect to the image content. Multiple times embedding of watermark ensures robustness against common signal processing operations (filtering, enhancement, rescaling etc.) and various attacks. The proposed algorithm is tested for 50 different types of host images and public data collection, DB3, FVC2002. FAR and FRR are compared with other methods to show the improvement.
Image steganography is the art of hiding a message, image, or file within another message, image, or file. Likely, an old term in Ancient Greek, Steganography is derived from steganos meaning ―”concealed” and graphein meaning ―”writing”, in other word we can say it refers to the science of “invisible” communication. Unlike cryptography, where the goal is to secure communications from an eavesdropper, steganography techniques strive to hide the very presence of the message itself froman observer. In this research paper we deal with hiding a digital message image inside a digital cover image leading us to the stego image. With the combination of Message Preparation using Spatial Domain image modification technique, discrete cosine transforms (DCT) and image scrambled using modified Arnold Transform, an algorithm based on the three technologies is proposed. The effectiveness of the proposed methods has been estimated by computing Mean square error (MSE) and Peak Signal to Noise Ratio (PSNR) and experimental result shows that the proposed algorithm is highly secured with good perceptual invisibility
A Survey Based on Fingerprint Matching SystemIJTET Journal
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PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITION
1. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
DOI: 10.5121/ijcsity.2018.6301 1
PREPROCESSING ALGORITHM FOR DIGITAL
FINGERPRINT IMAGE RECOGNITION
Farah Dhib Tatar1
and Mohsen Machhout2
1
Department of Electrical Engineering
National school of the studies of engineer of Tunis, Tunis, Tunisia
2
Faculty of Sciences of Monastir, Monastir, Tunisia
ABSTRACT
Biometrics is one of the most used technologies in the field of security due to its reliability and
performance. It is based on several physical human characteristics but the most used technology is the
fingerprint recognition, and since we must carry out an image processing to be able to exploit the data in
each fingerprint we propose in this article an image preprocessing procedure in order to improve its
quality before extracting the necessary information for the comparison phase.
KEYWORDS
Fingerprint Recognition Algorithm; Image processing; Image sensor; Image filtering
1. INTRODUCTION
The reliable identification of people is becoming more and more necessary to solve problems of
access and identity theft [1].
Access control is a requirement especially to sensitive places that require a high level of
confidentiality. The identification keys are of three main types: keys based on the knowledge of
passwords or encrypted codes, others based on the possession of access cards badges, keys, etc.,
or we can identify us based on the physical characteristics of person: these are biometric
identification techniques
Unlike the first two methods that can pose serious falsification problems, biometric identification
techniques are directly related to people's physical characters and therefore can not be copied or
desired.
The goals of a biometric security system can be multiple. A security system can, for example, use
the tools that biometrics makes available to manage the security of logical access of a system.
This is probably the most common use of biometrics. But it can also be used to secure the
physical access of a room containing sensitive data, from any place on which we choose to install
a biometric lock*.
------
*: http://www.agent-de-securite.be/biometrie/securite_biometrique.html
2. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
2
The biometric identification techniques are various: we can analyze the shape of the hand [2], the
design of the iris [3], the voice, the vascularization of the retina and the shape of the face. [4]
Likewise, the dynamic recognition of the signature [5], analyzed in real time (speed, pressure on
the pen, etc).
Given its low cost, ease of operation and reliability of fingerprint recognition results, this
technology occupies more than two-thirds of the biometric market.
Fingerprint recognition algorithms are diverse and are based on different techniques in order to
extract useful information from the input image. Obviously, the clearer and better the image, the
more reliable and fast the system is. An image sensor is composed of many photosites, each
corresponding to a picture element, it is the "pixel".Each pixel of an image sensor records the
amount of light to which it is exposed and converts it into a corresponding number of electrons.
The stronger the light, the higher the number of electrons generated.
Sensors embedded in fingerprint recognition systems are diverse and based on several
technologies, noting for example:
CCD sensors: they have been used in cameras for over 30 years and have many
advantages. In general, they still offer a slightly higher sensitivity to light than CMOS
sensors and produce a little less noise. However, they are more expensive and more
difficult to integrate into a camera.
CMOS sensors: they reduce the overall price of cameras, as they contain all the elements
necessary for the composition of cameras. They offer more integration possibilities and
more functions than CCD sensors. They also offer shorter measurement times, lower
power dissipation at the chip, and reduced footprint.
Megapixel sensors (sensors containing one million pixels or more): They are integrated
with megapixel cameras and are of a size equal to or only slightly larger than VGA sensors
that provide a resolution of 640x480 (307,200) pixels. This means that the size of each
pixel on a megapixel sensor is smaller than that on a VGA sensor. Therefore, although the
megapixel camera provides higher resolution and more detail, it is less sensitive to light
than its VGA counterpart since the pixel size is smaller and the light reflected from an
object is spread over more pixels.
Whatever the technology used, the purpose of a fingerprint recognition system is to have reliable
authentication and fast error rate as small as possible. For this purpose, we present in this paper a
preprocessing procedure of the captured image in order to increase its quality before proceeding
to the extraction step of useful information and consequently increase the overall performance of
the system by minimizing the total processing time as well as the rate of erroneous results.
2. PREPROCESSING PROCEDURE FOR DIGITAL FINGERPRINT
RECOGNITION
Digital fingerprint recognition is the most widely used biometric technique because of its ease of
use and demonstration. The chains of recognition by digital processes are based on image
3. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
3
processing techniques making it possible to extract the useful information of each image
(fingerprint) in order to be able to identify it and consequently compared it to other fingerprints.
Our fingerprints are unique, at least on certain points: these are called minutiae, that is to say, the
lines, the bifurcations, the "islets", the points, the ending of the peaks. The design formed by
these lines on our fingers never evolves. Depending on the level of security desired, it is
estimated that between 10 and 20 points of correspondence are required to certify a fingerprint.
A. Description of the fingerprint
A fingerprint consists of a set of locally parallel lines forming a single pattern for each individual
(Figure 1), there are streaks (or ridges, these are the lines in contact with a surface to the touch)
and the grooves (this are the hollows between two streaks). **
Fig. 1. Fingerprint characteristic
Each imprint has a set of global (centers and deltas) and local (minutiae) singular points (Figure
2).
Fig. 2. Local and Global singular points in a fingerprint
The centers correspond to places of convergence of the striations while the deltas correspond to
places of divergence.
Fingerprint recognition algorithms use several filtering methods in order to locate the useful
points for each fingerprint. The most used filter is that of Gabor given its proven performance in
this type of treatment.
------
**http://www.orcanthus.com/main/product/product.php?idpr=4
4. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
4
However, in order to have reliable results, the input image of the filter must be as clear as
possible, for this reason we propose a series of mathematical and morphological operations to
improve the appearance of the image before pass to the filtering step.
When capturing a fingerprint image, several factors affect image quality such as:
Scars in the finger, age of the person.
Parasitic substances (water, grease, dirt, etc.)
Capture environment: temperature (dilation, compression), humidity
These various factors greatly affect the quality of the input image of the system and consequently
increase the error rate and reduce the overall performance of the application; We must therefore
perform a specific treatment in order to reduce the parasitic effects and improve the image quality
used. The different pretreatment phases are presented in Figure 3.
Fig. 3. Preprocessing steps
B. Grayscale transformation
A fingerprint sensor in general returns a color image. For this algorithm, there is no need for color
schemes for processing. We must therefore make a transformation into grayscale. This
transformation allows us to reduce the space used for recording data included in the image; in
fact, the color pixels require 24 bits to be able to represent the different levels of colors, on the
other hand in grayscale we only need 8 bits for each pixel since we have only 256 gray levels. this
operation is performed according to the following formula (1):
I=(R+G+B)/3 (1)
Where I is the value of the pixel in gray and R, G and B are the values of the color components.
C. Image normalization
The normalization operation makes it possible to increase the contrasts in the image by making a
certain adjustment to the gray levels represented in each pixel without affecting the useful
5. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
5
information included in the fingerprint image.This operation is done locally on each block
according to the following steps:
Calculation of the average:
=
×
∑ ∑ , (2)
I (i, j) is the value of the pixel (i, j) and M is the value of the average of the image while m and n
are the dimensions of the image.
Calculation of variance:
= ×
∑ ∑ , − (3)
V is the variance of the image.
Calculation of the standardized gray level value of the pixel (i, j) [6]:
, =
+
× ,
, >
−
× ,
, <
! (4)
M0 and V0 are the desired values for mean and variance, respectively. We chose _0=100 and
V0=100.
Normalization does not change the structure of the image, but it is used to standardize the variation
of gray levels.
D. Segmentation
This operation aims to minimize the noise present in the image as well as to center the useful
information that will be used in the comparison phase of the different signatures, so we eliminate
the noisy areas as well as the useless edges of the image because they can lead us to make false
comparisons.
The results of this step is a binary image of the same size as the original image and called "Mask",
the block corresponding to the bottom of the image will be filled with 0 in the mask, and the rest
will be filled by 1.This is not all to satisfactorily because many blocks containing no useful
information persist.
To avoid this we decided to eliminate the blocks belonging to the bottom of the image according to
their average value. The local average value of each block Ml is calculated and compared to a
threshold T:
If " < # then we consider that the block contains no useful information and it is
eliminated.
6. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
6
If " > # then the block contains part of the fingerprint and is retained.
The threshold T has been chosen according to the average value M& of the overall image such that
=
'(
)
.
The mask then undergoes two treatments:
Any non-noisy block with at least 3 adjacent blocks defined as noisy is eliminated (this
corresponds to a strongly noisy area or the edge of the fingerprint.
The set of blocks at the edge of the image is deleted.
An example of segmentation is shown in Figure 4 [7].
Fig. 4. Effect of segmentation on the image
E. Spatial estimate of the directional map
To obtain the directional map, we goes through two stages: the estimation of the orientation and
the smoothing of the directional map.
1) Estimation of the orientation:
The directional map present the orientation of each streak contained in the fingerprint.Orientation
estimation is a fundamental step in Gabor's image-based image enhancement process. (Figure 5).
Fig. 5. Local orientation of a pixel
7. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
7
We present here the optimized calculation of the directional map of the fingerprint [7]. This
measurement must be reliable because, in the case of directional filtering, a poor evaluation of the
direction would cause a degradation of the image. We use here the method based on the least
square approximation ([8], [9]) which is very effective in the case of very noisy images.
To calculate the direction associated with a pixel x, y of the image, we consider its neighborhood
V of size W x W pixels. We calculate the gradients, *+ , in x and *, , in y for each pixel i,
j of neighborhood V. For the calculation of the gradient, the use of Sobel masks proved to be the
most efficient in the case of our images. Thus the local direction in neighborhood V is estimated
by the following relation (the proof of this formula is detailed in the article of M. Bazen and H.
Gerez [10].
- +, , = ./0 1
∑ ∑ . *+ , . *, ,
3
3
∑ ∑ 4*+ , − *, , 5
3
3
6
3 78 *+ = +, , × 9
−
−
−
:
; < *, = +, , × 9
− − −
: (5)
It is thus possible to calculate the local direction at each pixel of the image but this is very
expensive in computation time, so it is much more efficient to divide the image into adjacent
blocks and evaluate the local mean direction to the block leaves to interpolate the direction for
each pixel.
2) Refinement of the directional map
In the case where the input image is too noisy we can have false results when detecting the local
directions of the pixels. Thus we can note the presence of vertical lines at the directional map
levels that have no direction.
An example of the presence of vertical lines is illustrated in the figure (6) presenting the case of
estimation of the directional map with an “Arch” type fingerprint.
Fig. 6. Influence of vertical lines
8. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
8
This then causes a very large angular variation between two adjacent blocks. However, a
fingerprint has some directional continuity, such a variation between two adjacent blocks is then
representative of a poor estimate. To eliminate such discontinuities, a low-pass filter is applied to
the directional map. Applying a low-pass filter requires that the orientation of the image be
converted into a continuous vector field. This vector field has as components x and y respectively
defined by:
=+ , = >?@ . - , ; < =, , = @A0 . - , (6)
With the two components of the vector obtained, one can apply the Gaussian low pass filter of size
W_θ × W_θ defined by:
=+
B
, = C C * D, E . =+ − E. 3, − E. 3
3-
E
3-
3-
D
3-
=,
B
, = ∑ ∑ * D, E . =, − E. 3, − E. 3
3-
E
F3-
3-
D
F3-
(7)
Where G is the Gaussian low pass filter and w is the block size.
Finally, the local orientation smoothed at the pixel (i,j)is given by:
G , = ./0
=,
H ,
=+
H ,
(8)
The Figure (7) shows the example of the estimation of the directional map for different types of
fingerprint.
Fig. 7. Estimation of the directional map for different types of fingerprint
9. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
9
A Spatial estimate of the frequency map
The frequency map of the image consists in estimating the local frequency of the striations in each
pixel.
1) Frequencyof each block
The frequency mapis an image of the same size as that of the fingerprint and represents the local
frequency of the streaks. The first thing to do is to divide the image into sub-blocks of size
W × W. Then, we have to project each pixel in the block orthogonal to its direction (that is to say
in the direction 4
J
)
− θ5 ), we obtain a vector V presenting a set of extrema (figure 8) [10].
Fig. 8. Extrema obtained after projection
2) Spatial estimation of frequency
In the extrema vector obtained, the maxima represent the centers of the striations and the minima
correspond to the centers of the valleys. The inter-striated local period is then estimated by
calculating the average distance between two consecutive maxima S (i,j):
L , =
M ,
(9)
The maxima M_i and the minima m_i are determined by detecting the zero crossing of the
derivative of the vector V, which makes it possible to obtain the sequence
{M_1,m_1,……..,M_k,m_k,M_(k+1) }. If the difference between a maximum M_i and a
minimum m_i is less than a threshold T then we consider that M_i corresponds to a noise and it is
eliminated.
If the resultant vector contains at least two maxima then the inter-strikethrough period is calculated
by averaging the distances between two consecutive maxima, otherwise the period takes the value
of zero. When the estimated period is zero this means that the block contains no streaks
(background image) or that it is too noisy to reliably estimate the local frequency.
3. RESULTS AND DISCUSSION
We used Matlab to validate all this steps; the results are shown in the following pictures:
10. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
10
A. Applying the mask on the image:
Fig. 9. Mask of the image
This step allows us to eliminate the edges of the image as well as the very noisy areas.
B. Extraction of the directional map
Fig. 10. Directional map of the fingerprint
The directional map defines the local orientation of streaks contained in the fingerprint.
C. Extraction of the frequency card
Fig. 11. Frequency map of the fingerprint
11. International Journal of Computational Science and Information Technology (IJCSITY) Vol.6, No.1/2/3, August 2018
11
The frequency map estimates the local frequency of the streaks in each pixel.
In order to highlight the importance of image preprocessing we have considered a database based
on a hundred images captured by different types of sensors, in the first place we tried to go
directly to the stage filtering without making any changes to the images, we extracted the useful
information from each fingerprint, and then we passed to the authentication phase.
Second, we went through the preprocessing of images before filtering and the steps that follow;
the different results are displayed in the following table:
Table 1. Result of Preprocessing Application
Image without
pretreatment
Images with
preprocessing
Total number of
images
100 100
Rate of
erroneous
results
46 22
We therefore note that thanks to the various operations carried out to improve the quality of the
images, the rate of the erroneous results is reduced by 24%.
The improvements noted make the system more efficient from a point of view of reliability; the
criterion of speed can not be discussed in this step since the total processing time depends directly
on the type of processor used and therefore it varies according to the hardware platform used to
implement the fingerprint recognition algorithm.
4. CONCLUSION
Some are the type of sensor used or the algorithm principle applied; the image quality to be
processed remains a very important factor that directly affects the overall system performance.
For this fact, we have proposed in this article a set of operations to apply to the image in order to
increase its quality to be able to facilitate the different phases of treatment that follow.
The results obtained are motivating in terms of total error rate reduction, however, the total
processing time remains directly related to the type of hardware platform used.
REFERENCES
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