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.
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
Hybrid fingerprint matching algorithm for high accuracy and reliabilityeSAT 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.
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.
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.
Generate a key for MAC Algorithm using Biometric Fingerprint ijasuc
known as cryptographic checksum or MAC that is appended to the message.
The unauthorized thefts in our society have made the requirement for reliable information security
mechanisms. Information security can be accomplished with the help of a prevailing tool like cryptography,
protecting the cryptographic keys is one of the significant issues to be deal with. Here we proposed a
biometric-crypto system which generates a cryptographic key from the Finger prints for calculating the
MAC value of the information we considered fingerprint because it is unique and permanent through out a
person’s life.
This document summarizes a research paper on gesture recognition techniques for controlling mouse events without physically touching a mouse. The paper presents a technique using color detection and tracking of colored caps on fingers. By analyzing the number and positions of color regions in camera frames, various mouse gestures can be recognized, such as left click, right click, drag, etc. An algorithm was implemented in MATLAB using color space conversion from RGB to YCbCr to track hand gestures. Experimental results showed high recognition rates for common mouse events like cursor movement, clicking, and dragging. The technique provides an accessible way for people with disabilities to control computing devices through natural hand gestures.
Gesture Recognition Review: A Survey of Various Gesture Recognition AlgorithmsIJRES Journal
This document reviews various algorithms for gesture recognition from images and video. It discusses approaches such as pixel-by-pixel comparison, edge detection, orientation histograms, thinning, hidden Markov models, color space segmentation using YUV and tracking using CAMSHIFT, naive Bayes classification, 3D hand modeling, appearance-based modeling using eigenvectors, finite state machines, and particle filtering using condensation. It evaluates these methods and concludes that combining YUV segmentation, CAMSHIFT tracking and hidden Markov modeling provides an effective approach for hand detection and gesture recognition.
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
Hybrid fingerprint matching algorithm for high accuracy and reliabilityeSAT 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.
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.
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.
Generate a key for MAC Algorithm using Biometric Fingerprint ijasuc
known as cryptographic checksum or MAC that is appended to the message.
The unauthorized thefts in our society have made the requirement for reliable information security
mechanisms. Information security can be accomplished with the help of a prevailing tool like cryptography,
protecting the cryptographic keys is one of the significant issues to be deal with. Here we proposed a
biometric-crypto system which generates a cryptographic key from the Finger prints for calculating the
MAC value of the information we considered fingerprint because it is unique and permanent through out a
person’s life.
This document summarizes a research paper on gesture recognition techniques for controlling mouse events without physically touching a mouse. The paper presents a technique using color detection and tracking of colored caps on fingers. By analyzing the number and positions of color regions in camera frames, various mouse gestures can be recognized, such as left click, right click, drag, etc. An algorithm was implemented in MATLAB using color space conversion from RGB to YCbCr to track hand gestures. Experimental results showed high recognition rates for common mouse events like cursor movement, clicking, and dragging. The technique provides an accessible way for people with disabilities to control computing devices through natural hand gestures.
Gesture Recognition Review: A Survey of Various Gesture Recognition AlgorithmsIJRES Journal
This document reviews various algorithms for gesture recognition from images and video. It discusses approaches such as pixel-by-pixel comparison, edge detection, orientation histograms, thinning, hidden Markov models, color space segmentation using YUV and tracking using CAMSHIFT, naive Bayes classification, 3D hand modeling, appearance-based modeling using eigenvectors, finite state machines, and particle filtering using condensation. It evaluates these methods and concludes that combining YUV segmentation, CAMSHIFT tracking and hidden Markov modeling provides an effective approach for hand detection and gesture recognition.
Enhanced Thinning Based Finger Print RecognitionIJCI JOURNAL
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
This document discusses a hand gesture recognition system for underprivileged individuals. It begins by outlining the key steps in hand gesture recognition systems: image capture, pre-processing, segmentation, feature extraction and gesture recognition. It then goes into more detail on specific techniques for each step, such as thresholding and edge detection for segmentation. The document also covers applications like access control, sign language translation and future areas like biometric authentication. In conclusion, it proposes that hand gesture recognition can help disabled individuals communicate through accessible human-computer interaction.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It provides a concise way to represent templates and allows for faster matching by first comparing singular point information before minutiae points.
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.
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.
A Comparative Study of Fingerprint Matching AlgorithmsIRJET Journal
This document summarizes and compares several fingerprint matching algorithms. It begins with an introduction to fingerprint-based identification and authentication, describing how fingerprints provide a unique biometric for verifying identity. The document then reviews three specific fingerprint matching algorithms: 1) Ratio of Relational Distance Matching, which uses minutiae points and distance ratios to match fingerprints; 2) K-Nearest Neighbor Minutiae Clustering, which clusters fingerprint graphs using KNN before matching; and 3) Minutiae Extraction and Matching Algorithm, which extracts minutiae points through a multi-step process of binarization, thinning, connecting, and margin increasing. The document concludes by noting each algorithm has advantages and disadvantages depending on the application
14 offline signature verification based on euclidean distance using support v...INFOGAIN PUBLICATION
In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value.
Improvement of the Fingerprint Recognition Processijbbjournal
This document proposes improvements to fingerprint recognition processes. It summarizes a fingerprint recognition algorithm that includes pre-processing the fingerprint image using techniques like grayscale transformation, normalization, segmentation, and Gabor filtering to improve image quality. It then extracts biometric data by binarizing the image, skeletonizing it, and detecting minutiae points using the Crossing Number method. The algorithm is validated using Matlab to retrieve and analyze results.
Design of Gabor Filter for Noise Reduction in Betel Vine leaves Disease Segme...IOSR Journals
This document describes a design of a Gabor filter for noise reduction in images of betel vine leaves to aid in disease segmentation. A Gabor filter is designed using Verilog HDL and implemented on a CADENCE platform. The filter takes pixel inputs from images that have undergone preprocessing like Sobel edge detection and segmentation. It convolves the pixels with stored filter coefficients to reduce noise and segment the diseased areas. The proposed Gabor filter achieves noiseless segmentation with increased speed and reduced delays compared to existing methods. It utilizes fewer resources with minimal warnings. The system could be enhanced further with 2D/3D processing and neural network training.
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
Blending of Images Using Discrete Wavelet Transformrahulmonikasharma
The project presents multi focus image fusion using discrete wavelet transform with local directional pattern and spatial frequency analysis. Multi focus image fusion in wireless visual sensor networks is a process of blending two or more images to get a new one which has a more accurate description of the scene than the individual source images. In this project, the proposed model utilizes the multi scale decomposition done by discrete wavelet transform for fusing the images in its frequency domain. It decomposes an image into two different components like structural and textural information. It doesn’t down sample the image while transforming into frequency domain. So it preserves the edge texture details while reconstructing image from its frequency domain. It is used to reduce the problems like blocking, ringing artifacts occurs because of DCT and DWT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. It indicates the overall active level of an image. The high frequency sub-band coefficients are fused by selecting coefficients having maximum LDP code value LDP computes the edge response values in all eight directions at each pixel position and generates a code from the relative strength magnitude. Finally, fused two different frequency sub-bands are inverse transformed to reconstruct fused image. The system performance will be evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT 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.
This document presents a method for tracking moving objects in video sequences using affine flow parameters combined with illumination insensitive template matching. The method extracts affine flow parameters from frames to model local object motion using affine transformations. It then applies template matching with illumination compensation to track objects across frames while being robust to illumination changes. The method is evaluated on various indoor and outdoor database videos and is shown to effectively track objects without false detections, handling issues like illumination variations, camera motion and dynamic backgrounds better than other methods.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT 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
Automatic Image Annotation Using CMRM with Scene InformationTELKOMNIKA JOURNAL
Searching of digital images in a disorganized image collection is a challenging problem.
One step of image searching is automatic image annotation. Automatic image annotation refers
to the process of automatically assigning relevant text keywords to any given image, reflecting
its content. In the past decade many automatic image annotation methods have been proposed
and achieved promising result. However, annotation prediction from the methods is still far from
accurate. To tackle this problem, in this paper we propose an automatic annotation method
using relevance model and scene information. CMRM is one of automatic image annotation
method based on relevance model approach. CMRM method assumes that regions in an image
can be described using a small vocabulary of blobs. Blobs are generated from segmentation,
feature extraction, and clustering. Given a training set of images with annotations, this method
predicts the probability of generating a word given the blobs in an image. To improve annotation
prediction accuracy of CMRM, in this paper we utilize scene information incorporate with
CMRM. Our proposed method is called scene-CMRM. Global image region can be represented
by features which indicate type of scene shown in the image. Thus, annotation prediction of
CMRM could be more accurate based on that scene type. Our experiments showed that, the
methods provides prediction with better precision than CMRM does, where precision represents
the percentage of words that is correctly predicted.
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.
This document summarizes the privacy and security issues surrounding RFID implementations. It begins with an overview of RFID technology, including its history and how it works. It then discusses several current uses of RFID, such as in toll booths, financial transactions, supply chain management, and healthcare. Potential privacy issues with RFID include tracking individuals via implanted or external RFID tags. Security issues involve cloning RFID tags. The document analyzes these issues through two case studies: issues with the Mifare Classic RFID system and privacy/security vulnerabilities in US/Australian ePassports that use RFID.
The document discusses RFID technology, including how it works, its benefits and threats, and security considerations. RFID uses radio waves to read tags attached to objects without needing direct contact or line-of-sight. There are two types of tags - active tags with batteries and passive tags without. The document outlines security risks like spoofing, replay attacks, and unauthorized tracking. It emphasizes the need for lightweight cryptography and random number generation on tags to address security challenges in RFID systems.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
The document discusses how personalization and dynamic content are becoming increasingly important on websites. It notes that 52% of marketers see content personalization as critical and 75% of consumers like it when brands personalize their content. However, personalization can create issues for search engine optimization as dynamic URLs and content are more difficult for search engines to index than static pages. The document provides tips for SEOs to help address these personalization and SEO challenges, such as using static URLs when possible and submitting accurate sitemaps.
Enhanced Thinning Based Finger Print RecognitionIJCI JOURNAL
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
This document discusses a hand gesture recognition system for underprivileged individuals. It begins by outlining the key steps in hand gesture recognition systems: image capture, pre-processing, segmentation, feature extraction and gesture recognition. It then goes into more detail on specific techniques for each step, such as thresholding and edge detection for segmentation. The document also covers applications like access control, sign language translation and future areas like biometric authentication. In conclusion, it proposes that hand gesture recognition can help disabled individuals communicate through accessible human-computer interaction.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It provides a concise way to represent templates and allows for faster matching by first comparing singular point information before minutiae points.
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.
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.
A Comparative Study of Fingerprint Matching AlgorithmsIRJET Journal
This document summarizes and compares several fingerprint matching algorithms. It begins with an introduction to fingerprint-based identification and authentication, describing how fingerprints provide a unique biometric for verifying identity. The document then reviews three specific fingerprint matching algorithms: 1) Ratio of Relational Distance Matching, which uses minutiae points and distance ratios to match fingerprints; 2) K-Nearest Neighbor Minutiae Clustering, which clusters fingerprint graphs using KNN before matching; and 3) Minutiae Extraction and Matching Algorithm, which extracts minutiae points through a multi-step process of binarization, thinning, connecting, and margin increasing. The document concludes by noting each algorithm has advantages and disadvantages depending on the application
14 offline signature verification based on euclidean distance using support v...INFOGAIN PUBLICATION
In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value.
Improvement of the Fingerprint Recognition Processijbbjournal
This document proposes improvements to fingerprint recognition processes. It summarizes a fingerprint recognition algorithm that includes pre-processing the fingerprint image using techniques like grayscale transformation, normalization, segmentation, and Gabor filtering to improve image quality. It then extracts biometric data by binarizing the image, skeletonizing it, and detecting minutiae points using the Crossing Number method. The algorithm is validated using Matlab to retrieve and analyze results.
Design of Gabor Filter for Noise Reduction in Betel Vine leaves Disease Segme...IOSR Journals
This document describes a design of a Gabor filter for noise reduction in images of betel vine leaves to aid in disease segmentation. A Gabor filter is designed using Verilog HDL and implemented on a CADENCE platform. The filter takes pixel inputs from images that have undergone preprocessing like Sobel edge detection and segmentation. It convolves the pixels with stored filter coefficients to reduce noise and segment the diseased areas. The proposed Gabor filter achieves noiseless segmentation with increased speed and reduced delays compared to existing methods. It utilizes fewer resources with minimal warnings. The system could be enhanced further with 2D/3D processing and neural network training.
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
Blending of Images Using Discrete Wavelet Transformrahulmonikasharma
The project presents multi focus image fusion using discrete wavelet transform with local directional pattern and spatial frequency analysis. Multi focus image fusion in wireless visual sensor networks is a process of blending two or more images to get a new one which has a more accurate description of the scene than the individual source images. In this project, the proposed model utilizes the multi scale decomposition done by discrete wavelet transform for fusing the images in its frequency domain. It decomposes an image into two different components like structural and textural information. It doesn’t down sample the image while transforming into frequency domain. So it preserves the edge texture details while reconstructing image from its frequency domain. It is used to reduce the problems like blocking, ringing artifacts occurs because of DCT and DWT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. It indicates the overall active level of an image. The high frequency sub-band coefficients are fused by selecting coefficients having maximum LDP code value LDP computes the edge response values in all eight directions at each pixel position and generates a code from the relative strength magnitude. Finally, fused two different frequency sub-bands are inverse transformed to reconstruct fused image. The system performance will be evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT 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.
This document presents a method for tracking moving objects in video sequences using affine flow parameters combined with illumination insensitive template matching. The method extracts affine flow parameters from frames to model local object motion using affine transformations. It then applies template matching with illumination compensation to track objects across frames while being robust to illumination changes. The method is evaluated on various indoor and outdoor database videos and is shown to effectively track objects without false detections, handling issues like illumination variations, camera motion and dynamic backgrounds better than other methods.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT 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
Automatic Image Annotation Using CMRM with Scene InformationTELKOMNIKA JOURNAL
Searching of digital images in a disorganized image collection is a challenging problem.
One step of image searching is automatic image annotation. Automatic image annotation refers
to the process of automatically assigning relevant text keywords to any given image, reflecting
its content. In the past decade many automatic image annotation methods have been proposed
and achieved promising result. However, annotation prediction from the methods is still far from
accurate. To tackle this problem, in this paper we propose an automatic annotation method
using relevance model and scene information. CMRM is one of automatic image annotation
method based on relevance model approach. CMRM method assumes that regions in an image
can be described using a small vocabulary of blobs. Blobs are generated from segmentation,
feature extraction, and clustering. Given a training set of images with annotations, this method
predicts the probability of generating a word given the blobs in an image. To improve annotation
prediction accuracy of CMRM, in this paper we utilize scene information incorporate with
CMRM. Our proposed method is called scene-CMRM. Global image region can be represented
by features which indicate type of scene shown in the image. Thus, annotation prediction of
CMRM could be more accurate based on that scene type. Our experiments showed that, the
methods provides prediction with better precision than CMRM does, where precision represents
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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.
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Comparative study of various enhancement techniques for finger print imagesMade Artha
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Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
IMPROVEMENT OF THE FINGERPRINT RECOGNITION PROCESSADEIJ Journal
The increased development of IT tools and social communication networks has significantly increased the
need for people to be identified with reliable and secure tools hence the importance of using biometric
technology. Biometrics is an emerging field where technology improves our ability to identify a person. The
advantage of biometric identification is that each individual has its own physical characteristics that
cannot be changed, lost or stolen. The use of fingerprinting is today one of the most reliable technologies
on the market to authenticate an individual. This technology is simple to use and easy to implement. The
techniques of fingerprint recognition are numerous and diversified, they are generally based on generic
algorithms and tools for filtering images.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
"FingerPrint Recognition Using Principle Component Analysis(PCA)”Er. Arpit Sharma
Fingerprint recognition is one of the oldest and most popular biometric technologies and it is used in criminal investigations, civilian, commercial applications, and so on. Fingerprint matching is the process used to determine whether the two sets of fingerprints details come from the same finger or not. This work focuses on feature extraction and minutiae matching stage. There are many matching techniques used for fingerprint recognition systems such as minutiae based matching, pattern based matching, Correlation based matching, and image based matching.
A new method based upon Principal Component Analysis (PCA) for fingerprint enhancement is proposed in this paper. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. In the proposed method image is first decomposed into directional images using decimation free Directional Filter bank DDFB. Then PCA is applied to these directional fingerprint images which gives the PCA filtered images. Which are basically directional images? Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.
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.
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITION ijcsity
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.
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
PREPROCESSING ALGORITHM FOR DIGITAL FINGERPRINT IMAGE RECOGNITIONijcsity
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.
A Review Paper on Fingerprint Image Enhancement with Different MethodsIJMER
This document summarizes various techniques that have been used for fingerprint image enhancement in previous research. It discusses enhancement techniques in the spatial and frequency domains, as well as neural network-based and fuzzy-based approaches. Specifically, it reviews 12 different fingerprint enhancement algorithms proposed between 1994 and 2010. These algorithms use approaches such as directional filtering, Gabor filtering, median filtering, and genetic algorithms. The document evaluates each method and compares their performance based on metrics like minutiae extraction accuracy and false match rates. Overall, the document provides an overview of the state-of-the-art in fingerprint image enhancement techniques.
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.
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.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
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International Journal of Engineering and Science Invention (IJESI)
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 2 Issue 7 ǁ July. 2013 ǁ PP.31-49
www.ijesi.org 31 | Page
“Biometric Fingerprint”
1,
Aman Chandra Kaushik , 2,
Abheek Chaudhuri , 3,
Vandana Sharma, 4,
Paroma
Bhattacharya
1,2,3,4,
Department of Bioinformatics, University Institute of Engineering & Technology
Chhatrapati Shahu Ji Maharaj University, Kanpur-208024, Uttar Pradesh, India
ABSTRACT: Fingerprint-based identification is one of the most important biometric technologies which have
drawn a Substantial amount of attention. Humans have used fingerprints for personal identification for
centuries and the validity of fingerprint identification has been well established. In fact, fingerprint technology
is so common in personal identification that it has almost become the synonym of biometrics. Fingerprints are
believed to be unique across individuals and across fingers of same individual. Even identical twins having
similar DNA, are believed to have different fingerprints. These observations have led to the increased use of
automatic finger print based identification in both civilian and law-enforcement applications.
I. INTRODUCTION:
A fingerprint is the pattern of ridges and furrows on the surface of a fingertip. Ridges and valleys are
often run in parallel and sometimes they bifurcate and sometimes they terminate. When fingerprint image is
analyzed at global level, the fingerprint pattern exhibits one or more regions where ridge lines assume
distinctive shapes. These shapes are characterized by high curvature, terminations, bifurcations, cross-over etc.
These regions are called singular regions or singularities. These singularities may be classified into three
topologies; loop, delta and whorl. At local level, there are other important features known as minutiae can be
found in the fingerprint patterns. Minutiae mean small details and this refers to the various ways that the ridges
can be discontinuous. A ridge can suddenly come to an end which is called termination or it can divide into two
ridges which is called bifurcation.
Fingerprint matching techniques can be broadly classified as minutiae based and correlation based.
Minutiae based technique first locates the minutiae points in a given fingerprint image and matches their relative
placements in a stored template fingerprint. A good quality fingerprint contains between 60 and 80 minutiae, but
different fingerprints have different number of minutiae. The performance of minutiae-based techniques rely on
the accurate detection of minutiae points and the use of sophisticated matching techniques to compare two
minutiae fields which undergo non-rigid transformations. Correlation based techniques compare the global
pattern of ridges and valleys to see if the ridges in the two fingerprints align
Figure1: Finger rint Pattern
2. Biometric Fingerprint
www.ijesi.org 32 | Page
Figure1.1: Minutia (Valley is also referred as Furrow, Termination is also called Ending, and Bifurcation is also
called Branch).
Flow diagram of fingerprint recognition algorithm.
The fingerprint recognition problem can be grouped into two sub-domains: one is fingerprint verification and
the other is fingerprint identification (Figure 1.2.). In addition, different from the manual approach for
fingerprint recognition by experts, the fingerprint recognition here is referred as AFRS (Automatic Fingerprint
Recognition System), which is program-based.
3. Biometric Fingerprint
www.ijesi.org 33 | Page
Figure 1.2: Verification vs. Identification
Fingerprint verification is to verify the authenticity of one person by his fingerprint. The user provides
his fingerprint together with his identity information like his ID number. The fingerprint verification system
retrieves the fingerprint template according to the ID number and matches the template with the real-time
acquired fingerprint from the user. Usually it is the underlying design principle of AFAS (Automatic Fingerprint
Authentication System).Fingerprint identification is to specify one person‟s identity by his fingerprint(s).
Without knowledge of the person‟s identity, the fingerprint identification system tries to match his fingerprint(s)
with those in the whole fingerprint database. It is especially useful for criminal investigation cases. And it is the
design principle of AFIS (Automatic Fingerprint Identification System).
II. IMAGE ENCHANCEMENT
When an image is scanned from a finger print scanner the center of image and its support are quite
clear and easy to study. There is another part of image that is much darker and its size and other feature are not
easily discernable . In this the problem to enchance dark areas while leaving the other area as unchanged as
possible since it does not require enchancement.the objective of enhancement
is to improve the interpretability of the information present in images for human viewers.
Figure3: Input Fringer Print
4. Biometric Fingerprint
www.ijesi.org 34 | Page
Figure4: Enchance Fingerprint
J = imadjust(I,[low_in; high_in],[low_out; high_out]) maps the values in intensity image I to new values in J
such that values between low_in and high_in map to values between low_out and high_out. Values below
low_in and above high_in are clipped; that is, values below low_in map to low_out, and those above high_in
map to high_out.
Fingerprint Image Enhancement algorithm are employed to emphasize ,sharpen Or smoothen image features for
display & analysis. Image enhancement can be Classified in two broad categories as
1- Spatial domain method.
2- Transform domain method.
Spatial domain method operates directly on pixels. In our project we are using this method . In it we are using
histogram based operation because they are simple, fast & with them acceptable results for some applications
can be achieved.
2.1.Histogram -: The histogram of an image is a plot of the number of occurrences Of gray levels in the image
against the gray-level values. The histogram provides a convenient summary of the intensities in an image ,but it
is unable to convey any information regarding spatial relationships between pixels.
2.2.Histogram Equalization: Histogram equalization is to expand the pixel value distribution of an image so as
to increase the perceptional information. Equalization is a process that attempts to spread out the gray levels in
an image so that they are evenly distributed across their range. It assigns the brightness values of pixels based on
the mage histogram.It is a technique where the histogram of resultant image should be as flat as possible.
Figure3.1.1 the Original histogram of
a fingerprint image
Figure3.1.2 Histogram after
the Histogram Equalization
The right side of the following figure [Figure 3.1.1.3] is the output after the histogram equalization.
5. Biometric Fingerprint
www.ijesi.org 35 | Page
Figure3.1.3 Histogram Enhancement. Original Image (Left). Enhanced image (Right)
2.3.Fingerprint Image Binarization
Fingerprint Image Binarization is to transform the 8-bit Gray fingerprint image to a 1-bit image with 0-
value for ridges and 1-value for furrows. After the operation, ridges in the fingerprint are highlighted with black
color while furrows are white.
A locally adaptive binarization method is performed to binarize the fingerprint image. Such a named
method comes from the mechanism of transforming a pixel value to 1 if the value is larger than the mean
intensity value of the current block (16x16) to which the pixel belongs [Figure 3.2.1].
Figure3.2.1 the Fingerprint image after adaptive binarization Binarized image (left), Enhanced gray image
(right)
2.4.Segmentation
The first step of the fingerprint enhancement algorithm is image segmentation.Segmentation is the
process of separating the foreground regions in the image from the background regions. The foreground regions
correspond to the clear fingerprint area containing the ridges and valleys, which is the area of interest. The
background corresponds to the regions outside the borders of the fingerprint area, which do not
contain any valid fingerprint information. When minutiae extraction algorithms are applied to the background
regions of an image, it results in the extraction of noisy and false minutiae. Thus, segmentations employed to
discard these background regions, which facilitate the reliable extraction of minutiae.
In general, only a Region of Interest (ROI) is useful to be recognized for each fingerprint image. The image area
without effective ridges and furrows is first discarded since it only holds background information. Then the
bound of the remaining effective area is sketched out since the minutia in the bound region is confusing with
that spurious minutia that are generated when the ridges are out of the sensor.
To extract the ROI, a two-step method is used. The first step is block direction estimation and direction variety
check [1], while the second is intrigued from some Morphological methods.
6. Biometric Fingerprint
www.ijesi.org 36 | Page
2.4.Block direction estimation
Estimate the block direction for each block of the fingerprint image with WxW in size(W is 16 pixels by
default). The algorithm is:
[1] Calculate the gradient values along x-direction (gx) and y-direction (gy) for each pixel of the block. Two
Sobel filters are used to fulfill the task.
[2] For each block, use Following formula to get the Least Square approximation of the block direction.
[3] tg2ß = 2 (gx*gy)/ (gx2-gy2) for all the pixels in each block.
[4] The formula is easy to understand by regarding gradient values along x-direction and y-direction as
cosine value and sine value. So the tangent value of the block direction is estimated nearly the same as
the way illustrated by the following formula.
[5] tg2 = 2sin cos /(cos2 -sin2 )
[6] 1.2 After finished with the estimation of each block direction, those blocks without significant
information on ridges and furrows are discarded based on the following formulas:
E = {2 (gx*gy)+ (gx2-gy2)}/ W*W* (gx2+gy2)
For each block, if its certainty level E is below a threshold, then the block is regarded as a background block.
The direction map is shown in the following diagram. We assume there is only one fingerprint in each image.
Figure3.3.1.1 Direction map Binarized fingerprint (left), Direction map (right)
2.5.ROI extraction by Morphological operations
Two Morphological operations called „OPEN‟ and „CLOSE‟ are adopted. The „OPEN‟ operation can
expand images and remove peaks introduced by background noise [Figure 3.3.2.2]. The „CLOSE‟ operation can
shrink images and eliminate small cavities [Figure 3.3.2.3].
Figure 3.3.2.1 Original Image Area Figure 3.3.2.2 After CLOSE operation
Figure 3.3.2.3 After OPEN operation Figure 3.3.2.4 ROI + Bound
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Figure 3.3.2.4 show the interest fingerprint image area and its bound. The bound is the subtraction of the closed
area from the opened area. Then the algorithm throws away those leftmost, rightmost, uppermost and
bottommost blocks out of the bound so as to get the tightly bounded region just containing the bound and inner
area.
SELECTED QUERY FINGERPRINT
SEGMENTED QUERY FINGERPRINT
Figure5: Selected and Segmented Query Finger Print
2.6.Gabor Filter
Gabor filter method is assumed that the parallel ridges and valleys exhibit some ideal sinusoidal-shaped plane
waves associated with some noises. In other words, the 1-Dsignal orthogonal to the local orientation is
approximately a digital sinusoidal wave.
Figure6: Ridges and Valleys Pattern
Fingerprint images present a strong orientation tendency and have a well-defined spatial frequency in
each local neighborhood that does not contain singular point(s) Gabor filters are band-pass filters which have
both orientation-selective and frequency-selective properties and have optimal joint resolution in both spatial
and frequency domains. By applying properly tuned Gabor filters to a fingerprint image, the true ridge and
furrow structures can be greatly accentuated. These accentuated ridges and furrow structures constitute an
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efficient representation of a fingerprint image.An even symmetric Gabor filter has the following general form in
the spatial domain:
where f is the frequency of the sinusoidal plane wave along the direction q from the x-axis, and dx and dy
specify the Gaussian envelope along x and y axes, respectively, which determine the bandwidth of the Gabor
filter In our algorithm, the filter frequency f is set to the average ridge frequency (1/K), where K is the inter ridge
distance. The average inter ridge image. If f is too large, spurious ridges may be created in the filtered image,
whereas if f is too small, nearby ridges may be merged into one. The bandwidth of the Gabor filters is
determined by dx and dy. The selection of the values of dx and dy is based on the following trade-off. If they are
too large, the filter is more robust to noise, but is more likely to smooth the image to the extent that the ridge and
furrow details in the fingerprint are lost. On the other hand, if they are too small, the filter is not effective in
removing noise. In our algorithm, the values of dx and dy were empirically determined and both were set to 4.0.
If we decompose formula into two orthogonal parts, one parallel and the other perpendicular to the orientation /,
the following formula can be
The first part hx behaves as a 1-D Gabor function which is a band pass filter, and the second one hy
represents a Gaussian function which is a low pass filter Therefore, a 2-D even-symmetric Gabor filter(TGF)
performs a low pass filtering along the orientation / and a band pass filtering orthogonal to its orientation /. The
band pass and low pass properties along the two orthogonal orientations are very beneficial to enhancing
fingerprint images, since these images usually show a periodic alternation between ridges and valleys
orthogonal to the local orientation and parallel exhibit an approximate continuity along the local orientation.It
should be pointed out that hx in formula could be treated as a non-admissible mother wavelet (indicated by its
Fourier representation Its band pass property is related with the rx. If rx is too small, the band pass filter
degenerates into a low pass function. On the other hand, if rx is appropriately large, hx can be approximately
regarded as an admissible mother wavelet with good band pass property.The filter frequency f is set to the
average ridge frequency (1/K), where K is the inter ridge distance. The average inter ridge distance is
approximately 10 pixels in a 500 dpi fingerprint image. If f is too large, spurious ridges may be created in the
filtered image, whereas if f is too small, nearby ridges may be merged into one. The rx is the standard deviation
of the 2-D Gaussian function along the x-axis and ry along the y-axis. rx and ry control the spatial– frequency
bandwidth of the response. The larger they are, the wider bandwidth is expected. However, too wide bandwidth
can unexpectedly enlarge the noises, and too narrow bandwidth tends to suppress some useful signals. The value
of ry determines the smoothing degree along the local orientation. Too large ry can blur the minutiae. In our
algorithms, ry is empirically set as 4.0.
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Figure7: Gabor Filter Function
A fingerprint image is decomposed into four component images corresponding to four different values
of q (0o, 45o, 90o, and 135o) with respect to the x-axis. A fingerprint image is convolved with each of the four
Gabor filters to produce the four component images. Convolution with a 0o-oriented filter accentuates ridges
parallel to the x-axis, and it smoothes ridges which are not parallel to the x-axis. Filters tuned to other
directions work in a similar way. The four component images capture most of the ridge directionality
information present in a fingerprint image by reconstructing a fingerprint image by adding together all the four
filtered images. The reconstructed image is similar to the original image but the ridge has been enhanced before
decomposing the fingerprint image, we normalize the region of interest in each sector separately to a constant
mean and variance. Normalization is done to remove the effects of sensor noise and finger pressure differences.
Let I(x, y) denote the gray value at pixel (x, y), Mi and Vi, the estimated mean and variance of sector Si,
respectively, and Ni(x, y), the normalized gray-level value at pixel (x, y). For all the pixels in sector Si, the
normalized image is defined as:
Where M0 and V0 are the desired mean and variance values, respectively. Normalization is a pixel-wise
operation which does not change the clarity of the ridge and furrow structures.
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If normalization is done on the entire image, then it cannot compensate for the intensity variations in the
different parts of the finger due to finger pressure differences. Normalization of each sector separately alleviates
this problem. Ridgeshave been enhanced ridge and valley structures.
Figure8: Gabor Filter Output
III. TESSELLATION
A fingerprint consists of a series of ridges that mainly flow parallel to the locally dominant direction
and occasionally make local singularities, like a core or delta point. Since fingerprint patterns have strong
directionality, directional information can be exploited as fingerprint features. In this sense, a DFB is suitable
for extracting the features of fingerprints containing many linear and directional components because it can
effectively and accurately decompose an image into several directional sub band outputs. For feature extraction,
the original fingerprint image is decomposed into directional sub band outputs using the DFB, and the
directional energy of each block can be obtained from the decomposed sub band outputs.
Let denote the coefficient at position of sub band corresponding to an image block. The energy of the image
block associated with sub band is defined as
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Where Each energy value is calculated from the sub band corresponding to the image
block. The directional energy distributions of the sample fingerprint image blocks. We can see that by this
directional filtering operation the ridges and valleys became more distinct and noisy components were
suppressed at the same time.
Figure9: Tessellation
IV. FEATURE Extraction
In each component image, a local neighborhood with ridges and furrows that are parallel to the
corresponding filter direction exhibits a higher variation, whereas a local neighborhood with ridges and furrows
that are not parallel to the corresponding filter tends to be diminished by the filter which results in a lower
variation. The spatial distribution of the variations in local neighborhoods of theComponent images thus
constitute a characterization of the global ridge structures and are well captured by the standard deviation of
grayscale values. The standard deviation within the sectors defines the feature vector.
Let Ci (x, y) be the component image corresponding to for sector Si
Feature is the standard deviation Fi defined as
Where Ki is the number of pixels in Si and Miq is the mean of the pixel values in Ciq(x, y). The dimensional
feature vectors, also called Finger Codes
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V. FINGERPRINT ALIGNMENT AND MATCHING
Before the input print and template print are compared with each other, they should be translation ally
and rotationally aligned with each other. In the proposed method, the translational alignment is accomplished by
the reference point detection procedure for the ROI extraction processes. Rotational alignment is achieved by
generating cyclical input feature vectors and matching input feature vectors with template feature vectors. Since
the proposed feature extraction process is performed on a block-by-block basis and the directions of ridges
change smoothly, the proposed method is robust to small angular deviations, even without rotation
compensation. However if the input image is significantly rotated, the performance Degrades compensation for
rotation is partially achieved by cyclically rotating the feature values in the feature vector and finding the
minimum distance between the input and template feature vector. This only provides robustness for a small
perturbation with in, that is , half the angle of a sector. However, for this method to be invariant to smaller
perturbations , the extent of the rotation needs to be the same as the multiple of the angle of a sector, e.g., 22.5,
45 or many feature vectors for a variously rotated image must be enrolled in the database. To compensate
effectively for various kinds of rotation, the proposed method converts the ROI from Cartesian to polar
coordinates. In Cartesian coordinates, the rotation of an image changes the entire directional sub bands to totally
different ones, whereas in polar coordinates rotation corresponds to a horizontal shift in each directional sub
band output. Thus the coordinate system conversion facilitates the procedure of compensating for rotation.
Instead of using a single feature vector asthe input, the proposed method performs matching using several
feature vectors in which various rotations are considered. After an input fingerprint image is decomposed into
eight directional sub band outputs, the input feature vectors are obtained by cyclically shifting the sub band
images
Figure10: Feature vector
Fingerprint matching is performed based on finding the normalized Euclidean distance between the
input feature vectors and the template feature vector enrolled in the database. Let denote the -direction feature
value for the th block of the ROI for an input fingerprint image, in which degree rotation is considered and let
denote the -direction feature value for the th block of the ROI for the template fingerprint.
Then the normalized Euclidean distance between input and template image
and is the number of the feature vector elements where is not zero. Rotational and translational alignment
between the input and template fingerprints is achieved by finding the minimum normalized Euclidean distance
between the input and template feature vectors.
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User Manual
1. Type command start_gui_single_mode in MATLAB 6.0
Figure M.1 the User Interface of the Fingerprint Recognition System. The series of buttons on the left side will
be invoked sequentially in the consequent demonstration. The two blank areas are used to show the fingerprint
image before and after a transaction respectively.
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2. Click Load Button
Figure M.2 Load a gray level fingerprint image from a drive specified by Users. Multiple formats are supported
and the image size is not limited. But the fingerprint ridges should have large gray intensity comparing with the
background and valleys.
3. Click his-Equalization Button
Figure M.3 After Histogram Equalization. The image on the left side is the original fingerprint. The enhanced
image after the Histogram Equalization is shown on the right side.
4. Click fft Button
Figure M.4 Captured window after click „FFT‟ button. The pop-up dialog accepts the parameter k (please refer
the formula 2). The experimental optimal k value is 0.45. Users can fill any other constant in the dialog to get a
better performance. The enhanced image will be shown in the left screen box, which however is not shown here.
Its replaced by gabour
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5. Click Binarization Button
Figure M.5.6 Screen capture after binarization (left) and block direction estimation (right).
7. Click ROI Area Button
Figure M.7 ROI extraction(right). The intermediate steps for all the morphological operations such close and
open are not shown. The right screen box shows the final region of interest of the fingerprint image. The
subsequent operations will only operate on the region of interest.
8. Click Thinning Button
9. Click Remove H breaks Button
10. Click Remove spikes Button
Figure M.10 the Fingerprint image after thining, H breaks removal, isolated peaks removal and spike
removal.(right).
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11. Click Extract Button
Click Real Minutia Button
Figure M.12 Minutia marking (right) and False Minutia Removal (Left). Bifurcations are located with yellow
crosses and terminations are denotes with red stars. And the genuine minutia (left) is labeled with orientations
with green arrows.
12. Click Save Button
Figure M.13 Save minutia to a text file. The saved text file stores the information on all genuine minutia. The
exact format of the files are explained in the source code.
13. Click Match Button
Figure M.14 Load two minutia files and do matching. Users can open two minutia data files from the dialog
invoked by clicking the „Match‟ button. The match algorithm will return a prompt of the match score. But be
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noted that matching in the GUI mode is not encouraged since the match algorithm relies on heavy computation.
Unpredicted states will happen after a long irresponsive running time. Batch testing is prepared for testing
match. Please refer the source files for batch testing.
REFERENCES
[1] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, “An identity authentication
system using fingerprints,” Proc. IEEE, vol. 85, pp. 1365–1388, Sept. 1997.
[2] R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional
decomposition of images: Theory and design,” IEEE Trans. Signal
Processing, vol. 40, pp. 882–893, Apr. 1992.
[3] Almansa, A., Lindeberg, T., 2000. Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale
selection. IEEE Trans. Image Process.
[4] L. Hong, Y. Wan, and A.J. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation,” IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 20,no. 8, pp.777-789, August 1998.
[5] V. Areekul, U. Watchareeruetai, and S. Tantaratana, “Fast Separable Gabor Filter for Fingerprint Enhancement,” Proceeding
International Conference on Biometric Authentication (ICBA2004), LNCS3072, Springer, pp. 403-409, 2004
[6] A. Ross, A. K. Jain, and J. Reisman, "A Hybrid Fingerprint Matcher", Pattern Recognition, Vol. 36, No. 7, pp. 1661-1673, 2003.