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.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
The Image Panorama is a technique of stitching more images to create a more broader view which our normal eye does in a wider angle rather than that of the view which is restricted by the camera
Presentation on Face Recognition: A facial recognition is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
The Image Panorama is a technique of stitching more images to create a more broader view which our normal eye does in a wider angle rather than that of the view which is restricted by the camera
Presentation on Face Recognition: A facial recognition is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
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.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
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.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This paper introduces the implementation of fingerprint matching Minutiae Algorithm for Fingerprint
Matching. These algorithm increases the reliability accuracy of the fingerprint matching. The proposed method was
evaluated by means of experiment conducted on the FVC2002, FVC2004 database. Experimental results confirm
that the taking time of the fingerprint image matching is very less than the other methods. This algorithm is very
effective algorithm for the identification of fingerprint image.
ABSTRACT Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
A Novel 2D Feature Extraction Method for Fingerprints Using Minutiae Points a...IJECEIAES
The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.
Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks. Or Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
3. MATLAB SOFTWARE
MATLAB is a high-performance language for technical
computing. It integrates computation, visualization, and
programming in an easy-to-use environment where problems
and solutions are expressed in familiar mathematical notation.
Math and computation
Algorithm development
Data acquisition Modeling, simulation, and prototyping
Data analysis, exploration, and visualization
Scientific and engineering graphic
Application development, including graphical user
interface building.
4. INTRODUCTION
WHAT IS FINGERPRINT?
•An impression or mark made on a surface by a
person's fingertip.
•Able to be used for identifying individuals
from the unique pattern of whorls and lines on
the fingertips.
•Fingerprints are the tiny ridges, whorls and
valley patterns on the tip of each finger.
•Totally unique.
5. FINGERPRINT RECOGINITION
•Fingerprint recognition refers to the automated method of identifying or
confirming the identity of an individual based on the comparison of two
fingerprints.
•Fingerprint recognition is one of the most well known biometrics, and it is by far
the most used biometric solution for authentication on computerized system.
6. Fingerprint patterns
(a) Basic patterns
The three basic patterns of fingerprint ridges are:
•An arch is a pattern where the ridge enters one side of
the finger, then rises in the center forming an arch, and
exits on the other side of the finger.
•With a loop the ridge enters one side of the finger, then
forms a curve, and exits on the same side of the finger
from which it entered. Loops are the most common
pattern in fingerprints.
• Finally a whorl is the pattern you have when ridges
form circularly around a central point.
7. (b)Minutiae features
•Minutiae refer to specific points in a fingerprint,
these are the small details in a fingerprint that
are most important for fingerprint recognition.
•There are three major types of minutiae
features:
• The ridge ending,
•The bifurcation,
•The dot (also called short ridge).
•The ridge ending is, as indicated by the name,
the spot where a ridge ends.
•A bifurcation is the spot where a ridge splits
into two ridges. Spots are those fingerprint
ridges that are significantly shorter than other
ridges.
8. FINGERPRINT RECOGINITION TECHNOLOGY
Enrollment phase : In the enrollment
phase:
Sensor scans the fingerprint of user and
then determine the minutiae point .
Feature extractor extracts the minutiae
points from image
Then the system stores the minutiae
information along with demographic
information of the person as a template in
the database.
Recognition phase: In the recognition
phase,
Sensor generates the fingerprint image of
user namely as a query image.
Minutiae extractor will extracts the
minutiae points from query image and then
the matcher module compares the minutiae
points of user’s image with the stored
minutiae template in database and then a
match score is generated by the system.
After that, System determines the person’s
identity by comparing the obtained match
score with the threshold value.
The basic function of the fingerprint
recognition system.
A fingerprint recognition technology mainly works in two phase :
9. Fingerprint Recognition Steps:
The fingerprint classification based on mainly two strategies.
Local features : Local features can be characterized as minutiae points.
Global features : Global characteristics of fingerprint can be easily seen
with our eyes like pattern type, orientation, position, spatial frequency,
line types- dot, island, core, curvature points.
The main ingredients of thumb impression are defined by its type,
position of minutiae, orientation etc.
It is true that global attributes of more than one person are similar but
their local features can never be same.
Discovery of local features needs to determine the ridge ends or
bifurcation.
In order to extract these minutiae various steps must be followed:
10. (1) Image acquisition
In this step raw image of fingerprint is taken from source.
Fingerprint impression can be easily received when ridges and valleys of a
finger touches a surface.
Fingerprint may be scanned through off line fingerprint acquisition through
ink technique.
And system captures the impression then in matching phase fingerprint are
matched with large number of stored database.
Fingerprint verification generally uses two schemes minutiae based (local
representatives) and image based (whole features of global representatives).
11. A fingerprint image may be contaminated
due to noise present in image, holes, blurred
image, and smeared mark on surface,
corrupted devices or scanner.
Enhancement is necessary step to increase
contrast between ridges and furrows, to get
rid of unrecoverable regions, to improve
clarity of ridges minutiae and bifurcation,
speedy extraction of minutiae ,increase
brightness of an image, normalization,
orientation estimation, filtering of an
image, binarization and thinning steps.
The very first step in enhancement is
Normalization may be performed to check
image quality and also for easily comparison
with different modalities. database.
(2) Image Enhancement
12. Features extraction mainly uses three
approaches such as:
feature based method (global),
image based,
minutiae based features (local).
The local and global feature describe the
whole structure and characteristics of
impression such as loop, arch, whorl etc
patterns like two ridge meets, ridges singular
point, ridges count, core point, lines type etc
made up all these features.
The extracted features from each fingerprint
contain the orientation details like position of
center point, bifurcation and ridge ending and
Euclidean distance between feature vector
points.
INPUT
IMAGE
ENHANCEMEN
T
FEATURE
ECTRACTION
(3)Fingerprint Extraction
13. Binarization is a procedure by which the
grayscale image is changed over into a black
and white image. Generally 1 is taken as
white and 0 as black.
0-value for ridges and 1-value for furrows.
After the operation, fingerprint is highlighted
ridges in the fingerprint are featured with
black color while furrows are white for
processing.
(4)Image Binarization
INPUT
BINARIZATI
ON
14. In the biometric process of finger scanning, minutiae are specific points in a finger
image.
There are two main types, known as ridge endings and bifurcations. Sometimes,
other details, such as the points at which scars begin or terminate, are considered
minutiae.
The number and locations of the minutiae vary from finger to finger .
When a set of finger images is obtained from an individual, the number of minutiae
is recorded for each finger.
The precise locations of the minutiae are also recorded, in the form of numerical
coordinates, for each finger.
(5) Minutiae Extraction
15. (5.1) Image thinning
Fingerprint ridge thinning process eliminating the
redundant pixels of ridges and this will carried
out till the ridges are just one pixel wide in
length.
Removes noise as well.
Fingerprint image passed for orientation,
alignment checking process for discovery of
direction of each point where ridges are
ending,edges.
Two ridges are splitting and so on in thumb
impression.
(5.2) Terminations and bifurcations
matching
Various data acquisition conditions such as
impression pressure can easily change a minutia
form into a different one.
we adopt the elastic match representation for both
termination and bifurcation.
So each minutia is wholely described by its
coordinates (x,y) and its orientation.
16. (6)Minutiae Matching
One image chosen dynamically and another chosen
by system from database; now segmented image of
each block will be matched based on ridges
associated with two reference minutiae points .
Now calculate the matching minutiae in each block
coordinates; i.e. where x-axis coincident with the
reference segmented image with direction of
minutiae points.
In matching stage we actually evaluate the
matched minutiae pairs from two images nearly
with same position and direction are same.
Also in matching algorithm for different alignment
of impression from each person is taken then each
minutia in the template either matched within
rectangle box and orientation is slightly deformed.
17. PRINCIPLE COMPONENT ANALYSIS
PCA finds the principal components of data.
It is often useful to measure data in terms of its principal components rather than on a normal
x-y axis.
The main idea of PCA is to reduce the dimensionality of a data set consisting of many
variables correlated with each other, either heavily or lightly, while retaining the variation
present in the dataset, up to the maximum extent.
The same is done by transforming the variables to a new set of variables, which are known as
the principal components (or simply, the PCs) and are orthogonal, ordered such that the
retention of variation present in the original variables decreases as we move down in the order.
The principal components are linear combinations of the original variables weighted by their
contribution to explaining the variance in a particular orthogonal dimension.
18. Implementation of PCA
Step 1: Normalize the data
First step is to normalize the data that we have so that PCA works properly.
This is done by subtracting the respective means from the numbers in the
respective column. So if we have two dimensions X and Y, all X become 𝔁-
and all Y become 𝒚-.
This produces a dataset whose mean is zero.
Step 2: Calculate the covariance matrix
Since the dataset we took is 2-dimensional, this will result in a 2x2
Covariance matrix.
Please note that Var[X1] = Cov[X1,X1] and Var[X2] = Cov[X2,X2].
19. Step 3: Calculate the eigenvalues and eigenvectors
Next step is to calculate the eigenvalues and eigenvectors for the covariance
matrix. The same is possible because it is a square matrix. ƛ is an eigenvalue
for a matrix A if it is a solution of the characteristic equation:
det( ƛI - A ) = 0
Where, I is the identity matrix of the same dimension as A which is a
required condition for the matrix subtraction as well in this case and ‘det’ is
the determinant of the matrix. For each eigenvalue ƛ, a corresponding
Eigen-vector v, can be found by solving:
( ƛI - A )v = 0
20. Step 4: Choosing components and forming a
feature vector:
We order the eigenvalues from largest to smallest so that it gives us the
components in order or significance. Here comes the dimensionality
reduction part. If we have a dataset with n variables, then we have the
corresponding n eigenvalues and eigenvectors. It turns out that the
eigenvector corresponding to the highest eigenvalue is the principal
component of the dataset and it is our call as to how many eigenvalues we
choose to proceed our analysis with. To reduce the dimensions, we choose
the first p eigenvalues and ignore the rest. We do lose out some information
in the process, but if the eigenvalues are small, we do not lose much.
Next we form a feature vector which is a matrix of vectors, in our case, the
eigenvectors. In fact, only those eigenvectors which we want to proceed
with. Since we just have 2 dimensions in the running example, we can either
choose the one corresponding to the greater eigenvalue or simply take
both.
Feature Vector = (eig1, eig2)
21. Step 5: Forming Principal Components:
This is the final step where we actually form the principal
components using all the math we did till here. For the same, we take
the transpose of the feature vector and left-multiply it with the
transpose of scaled version of original dataset.
NewData = FeatureVectorT x ScaledDataT
Here,
NewData is the Matrix consisting of the principal components,
FeatureVector is the matrix we formed using the eigenvectors we
chose to keep, and
ScaledData is the scaled version of original dataset
(‘T’ in the superscript denotes transpose of a matrix which is formed
by interchanging the rows to columns and vice versa. In particular, a
2x3 matrix has a transpose of size 3x2).
22. All the eigenvectors of a matrix are perpendicular to each other. So, in PCA,
what we do is represent or transform the original dataset using these
orthogonal (perpendicular) eigenvectors instead of representing on normal x
and y axes.
We have now classified our data points as a combination of contributions
from both x and y.
The difference lies when we actually disregard one or many eigenvectors,
hence, reducing the dimension of the dataset.
Otherwise, in case, we take all the eigenvectors in account, we are just
transforming the co-ordinates and hence, not serving the purpose.