This document describes a proposed security system for ATMs that uses eye and facial recognition. It discusses how current ATM security with cards and PINs has vulnerabilities. The proposed system would use facial recognition software and iris scanning to identify users. It outlines some of the key techniques for facial recognition, including 2D, 3D, and surface texture analysis. It also explains how iris recognition works and how the integrated system would authenticate users via the camera before allowing transactions. In conclusion, it asserts that combining facial and iris biometrics with ATMs would significantly improve security over card and PIN alone.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
One of the most helpful presentation for academic and non academic purpose. This presentation can be presented for 40-45 mins. It contains both technical and non technical details of working of a fingerprint bio-metric scanner.
Star Link Communication Pvt. Ltd., India's leading manufacturer of biometric attendance system and access control system, brings you this slideshow about biometrics and how the technology works.
Presentation for September 2017 ISC2 Security Congress
Biometric Recognition for Multi-Factor Authentication
- Biological and Behavioral Biometrics
- Benefits and Issues
- What Every CISO Should Know
- Laws, Standards, and Guidelines
- How to Measure Biometric Recognition
- Attack Vectors
- Multimodal Biometric Recognition
- Continuous Authentication with Biometrics
- Face ID Update
- The Future
In the age of Biometric Security taking over the traditional security features, this is a small intro to the Biometric features one can use to enhance the security. The various modalities have been explained.
One of the most helpful presentation for academic and non academic purpose. This presentation can be presented for 40-45 mins. It contains both technical and non technical details of working of a fingerprint bio-metric scanner.
Star Link Communication Pvt. Ltd., India's leading manufacturer of biometric attendance system and access control system, brings you this slideshow about biometrics and how the technology works.
Presentation for September 2017 ISC2 Security Congress
Biometric Recognition for Multi-Factor Authentication
- Biological and Behavioral Biometrics
- Benefits and Issues
- What Every CISO Should Know
- Laws, Standards, and Guidelines
- How to Measure Biometric Recognition
- Attack Vectors
- Multimodal Biometric Recognition
- Continuous Authentication with Biometrics
- Face ID Update
- The Future
In the age of Biometric Security taking over the traditional security features, this is a small intro to the Biometric features one can use to enhance the security. The various modalities have been explained.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
IRIS Recognition Based Authentication System In ATMIJTET Journal
Security and Authentication of individuals is necessary for our daily lives especially in ATMs. It has been improved by using biometric verification techniques like face recognition, fingerprints, voice and other traits, comparing these existing traits, there is still need for considerable computer vision. Iris recognition is a particular type of biometric system that can be used to reliably identify a person uniquely by analyzing the patterns found in the iris. Initially Iris images are collected as datasets and maintained in agent memory. Then the Iris and pupil are detected from the image, removing noises. The features of the iris were encoded by convolving the normalized iris region with 2DGabor filter. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris.
It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. ATM Security Using Eye and Facial
Recognition System
ARUN KRISHNA K
20042425
2. CONTENTS
• Introduction
• Face recognition systems
• How do they work
• Methodology
• Techniques and Methods
• 2d Techniques
• 3d Techniques
• Surface-Texture-Analysis
• Iris Recognition
4. INTRODUCTION
The rise of technology into India has brought into force many type of equipment that
aim at more customer satisfaction. ATM is one such machine which made money
transaction easy for customers to the bank. But it has both advantages and
disadvantages. Current ATMs make use of naught more than an access card and PIN
for uniqueness confirmation. This has ATM Using Face Recognition System
demonstrate the way to a lot of fake attempt and mistreatment through card theft, PIN
theft, stealing and hacking of customers account details and other parts of security. This
process would effectively become details and other part of security. This process would
effectively become an exercise in pattern matching, which would not require a great
deal of time.
5. FACE RECOGNITION SYSTEMS
FRS is an application that mechanically identifies a person from a
digital image or a video outline from a video source. One of the behaviors
to do this method is by matching chosen facial features from a facial
database and the image. In this system, with appropriate lightning and
robust learning. Further a positive visual match would cause the live
image to be stored in the database so that future transactions would have
broader base from which to compare if the original account image fails to
provide a match –thereby decreasing false negatives.
7. HOW DO THEY WORK
A database of peoples face is maintained by the system that handles face
detection. There are typically 3 parts related to a face recognition:
• Face-detector
• Eye-localizer
• Face-recognizer
8. 1. Face-detector:The face detector spot the face, eliminating any other
detail, not related to the face (like the backdrop). It identifies the facial
region and leaves the non-facial region in the photo of the person to be
identified.
2. Eye-localizer: It finds the spot of the eyes; so that the position of the
face can be identify better
3. Recognizer: It will check the database to find a match.
9. METHODOLOGY
The first and foremost important step of this system will be to locate a
powerful open source facial recognition program that uses local feature
analysis and that is targeted at facial verification. Various facial recognition
algorithms be familiar with faces by extracting features, from a snap of the
subject's face. For ex, an algorithm may examine the size, relative position,
in addition to/or outline of the nose, eyes, cheekbone and jaw. These facial
appearances are then used to search for other imagery across matching
features. Other algorithm manages a balcony of face images and then
compresses the images face information and it saves only the data in the
image that is used for face detection.
11. 2-D-Technique
The 2-D recognition method was individual of the original techniques
employed. It maintained details of people‟s faces as seen two dimensionally.
Details like width of the nose, width of the eyes, distance between the eyes,
jaw line, cheek bone figure were used for contrast. This type of face
recognition was not too precise. Change in facial expression or difference in
ambient lighting on an appearance that is not directly looking into the
camera did not produce expected results.
12. 3-D-Technique
Progression in face recognition gave origin to the 3-D recognition system.
This stepped up technique, used facial appearance like contours of the eye
sockets, chin, nose, peaks and valley on the visage for identification. The
database will store details of faces also. The advantage of 3- D technique
over 2-D method is that 3-D face identification works fine even if the face is
turned at 90 degree to the camera. It is self-governing of lighting
environment and facial expressions.3-D-Technique
13.
14. Surface-Texture-Analysis
The most superior method is Surface Texture Analysis (STA). STA does
not examine the entire face but a patch of membrane on it. This patch is
divided into separate blocks. The skin surface, the pore on the skin and
other face characteristics are converted to a code. This code is used for
comparisonSurface-Texture-Analysis
15. IRIS RECOGNITION
In spite of all these security features; a new technology has been
developed. Bank United of Texas became the first in the United States to
offer iris recognition technology at automatic teller machines, providing the
customers a card less, password-free way to get their money out of an ATM.
there‟s no card to show, there's no fingers to ink, no customer
inconvenience or discomfort. It's just a photograph of a Bank United
customer's eyes. Iris recognition is an automated method of biometric
identification that uses mathematical pattern-recognition techniques on
video images of one or both the issues of an individual‟s eyes whose
complex patterns are unique, stable, and can be seen from some distance.
A key advantage of Iris recognition besides its speed of matching and its
extreme resistance to false matches, is the stability of the Iris as an internal
and protected, yet externally visible organ of an eye. Figure below shows an
schematic diagram of Iris recognition.
16. Just step up to the camera while your eye is scanned. The iris -- the colored
part of the eye the camera will be checking -- is unique to every person,
more so than fingerprints. And, for the customers who can't remember their
personal identification number or password and scratch it on the back of
their cards or somewhere that a potential thief can find, no more fear of
having an account cleaned out if the card is lost or stolen.
17.
18. HOW THE SYSTEM WORKS
When a customer puts in a bankcard, a stereo camera locates the face,
find the eye and take a digital image of iris at a distance of up to two to three
feet. The result computerized „iris code‟s compared with one of the
customer will initially provide the bank. ATM won‟t work if the two codes
don‟t match. The camera also does not use any kind of beam. Instead, a
special lens has been developed that will not only blow up the image of the
iris, but provide more detail when it does. Iris scans are more accurate than
other high-tech id system available that scan voices and fingerprints.
19. Conclusion
We thus develop an ATM model which provides security by using Facial
verification software Adding up facial recognition systems to the identity
confirmation process used in ATMs can reduce forced transactions to a
great extent. Using a 2d and 3d technology for identification is strong and it
is further fortified when another is used at auntheticiation level.