FACE RECOGNITION ACROSS NON-UNIFORM MOTION BLUR Koduru KrisHna
we will get the original image by giving the read command in the MAT LAB code. The remaining images are the illuminated image, blurred image, de-blurred image, illuminated blurred image which is modulated with the LBP technique, original image which is modulated with the LBP technique and the closest match gallery image. The closest match gallery image is obtained by comparing with all the images present in the database.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
FACE RECOGNITION ACROSS NON-UNIFORM MOTION BLUR Koduru KrisHna
we will get the original image by giving the read command in the MAT LAB code. The remaining images are the illuminated image, blurred image, de-blurred image, illuminated blurred image which is modulated with the LBP technique, original image which is modulated with the LBP technique and the closest match gallery image. The closest match gallery image is obtained by comparing with all the images present in the database.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
This application was design with help of OpenCv and C#.
Facial recognition (or face recognition) is a type of bio-metric application that can identify a specific individual in a digital image by analysing and comparing patterns.
Face recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If we look at the mirror, we can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features.
This application take picture of your face and after storing it.
Then it start identifying all face which are store in database.
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.
Facial Recognition: The Science, The Technology, and Market ApplicationsInvestorideas.com
Ravi Das
Technical Writer
BiometricNews.net
Ravi is a technical writer for BiometricNews.net, Inc., and independent news and information business about the Biometrics Industry. Ravi has been involved in Biometrics for 10+ years. He holds a BS in Ag Econ from Purdue, and MS in Ag Bus Economics (International Trade) from Southern Illinois University, Carbondale, and an MBA (MIS) from Bowling Green State University.
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์BAINIDA
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
This application was design with help of OpenCv and C#.
Facial recognition (or face recognition) is a type of bio-metric application that can identify a specific individual in a digital image by analysing and comparing patterns.
Face recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If we look at the mirror, we can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features.
This application take picture of your face and after storing it.
Then it start identifying all face which are store in database.
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.
Facial Recognition: The Science, The Technology, and Market ApplicationsInvestorideas.com
Ravi Das
Technical Writer
BiometricNews.net
Ravi is a technical writer for BiometricNews.net, Inc., and independent news and information business about the Biometrics Industry. Ravi has been involved in Biometrics for 10+ years. He holds a BS in Ag Econ from Purdue, and MS in Ag Bus Economics (International Trade) from Southern Illinois University, Carbondale, and an MBA (MIS) from Bowling Green State University.
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์BAINIDA
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
Machine Learning & IT Service Intelligence for the Enterprise: The Future is ...Precisely
Enterprises with mainframes and Cloud/server architectures face unique issues and challenges and if your enterprise delivers a service whose operation spans mainframe and distributed and/or Cloud infrastructures (e.g. a mobile banking/customer app), this webinar is for you.
See how you can gain unique business and service-relevant context using your own machine data, including that from your z/OS mainframe. Implicitly learn patterns, eliminate costly false alerts, identify anomalies, and baseline normal operations by employing advanced analytics driven by machine learning. You’ll also see and learn about:
• Accelerating root-cause analysis and getting ahead of customer-impacting outages and slow-downs for your service
• “Glass Table” view for clickable visualization of the entire service-relevant infrastructure
• Machine Learning in IT Service Intelligence
• The Machine Learning Toolkit available today
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Fighting Financial Crime with Artificial IntelligenceDataWorks Summit
How can we take the state of the art in deep learning and AI research, and transplant it into a large bank to deliver useful results which impact the general public? To answer this broad-reaching question, we take the viewer through a solution Think Big Analytics recently deployed at a major European bank for fraud detection, using state of the art AI techniques and a near-real time open-source architecture. We show how financial transactions can be transposed into a form where the latest AI techniques in image recognition can be leveraged, in surprisingly novel ways. We have been able to more accurately detect fraud and reduce financial crime, cutting losses and improving customer experience. We describe some architectures which can be used to do this in production, at scale, in global financial institutions.
Speaker:
Tim Seears, Director of Data Science, Think Big Analytics, a Teradata Company
Identifying unconscious patients using face and fingerprint recognitionAsrarulhaq Maktedar
The presentation is about our project which helps to identify any unconscious person with help of face or fingerprint recognition, which is based on biometrics.
The presentation also explains the algorithm we used in our project
SourceAFIS used for Fingerprint Recognition
CNN ( Convolution Neural Network ) used for Face Recognition
The presentation also includes IEEE Reference Papers
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.
Similar to Face recognition system using Hidden Markov Model (20)
Trade relations US & India; the changing facesCharmi Chokshi
this is a presentation on a different kind of trade relations between countries like US and India and their changing faces from years with the conclusion.
a presentation on a detailed analysis of product- Pencil in India as well as in the world with summary and plots and proper justification using economic theories.
An organized and systematic office solution is essential for all universities and organizations.
There are many departments of administration for the maintenance of college information and student databases in any institution.
All the modules in college administration are interdependent. So they need to be centralized as Information from one module will be needed by other modules.
The SEAS Educational Resource Management System is an automated version of manual system. In case of manual system they need a lot of time, manpower etc. Here all work is computerized. So the accuracy is maintained. Maintaining backup is very easy which it can do with in a few minutes.
this is a data structure and algorithm project which is made using TRIE data structure to help our user to find the meaning, synonyms, antonyms and examples of an entered word and if the user enters an invalid word then we help them to explore more words similar to the entered word.
A user can find the translation of the entered word, also view the search history and also know ‘word of the day’ which enables the user to learn a new word every day.
this is a presentation on 8-bit RISC-based single core pipelined microprocessor which can be used to perform various arithmetic and logical operations.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
2. Background and Motivation
• Today in this information era, most of data is secured by computers using different
security mechanisms
• Password
• Encryption key
• Palm/ Finger print
• Advantage of Face Recognition System: Non-Contact Process
• Core problem in Computer Vision & Image Processing
• Can be used to stop mischievous activities such as Proxy!
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3. Probability Model: Hidden Markov Model
• Markov Model: Future states depends on current states
• Hidden Markov Model:
• States and Observations are known
• But sequence of states is not defined to get a particular sequence of
observations
𝑆1:𝑘 = States & 𝑍1:𝑘= Observations 1- D HMM for Face Recognition
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5. Example
Rainy Sunny
0.40.6 0.8
0.2
Markov Model: Rainy
or Sunny depends on
current states
Happy Grumpy Happy Grumpy
Hidden Markov Model:
Observations happy or
grumpy depends on states 0.4 0.6 0.9 0.1
5/10May 8, 2017 Face Recognition System
𝑃 𝑅1 𝐻1 ) =
𝑃 𝐻1 𝑅1 ) ∙ 𝑃(𝑅1)
𝑃(𝐻1)
Assume: You are Happy on Day 1.
𝑃 𝑅0 = 𝑃 𝑆0 =
1
2
7. Algorithm
• Finding transition, emission probabilities and sequence of states:
• Using Viterbi or Baum-Welch Algorithm
• Likelihood of a states for a given sequence of observations
• Finding out the most likely states using previously maximised sequence of
states
• Using logarithm of sequence of observations find out the most probable state
May 8, 2017 Face Recognition System 7/10
8. Algorithm
• Use of MATLAB functions
• hmmtrain: find out transition and emission probabilities
• hmmdecode: conditional probabilities for each states and logarithm of
sequence of observation for given transition and emission probability
• hmmvertbi: find out sequence of states
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9. Results
• State transition and emission probabilities • Conditional probabilities for each states and logarithm
of probability of given sequence of observations for
given transition and emission probability
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10. • Creating database of 40 faces that includes
transition and emission probabilities calculated
using “hmmtrain” function
• Following are 10 different images of person s5, taken from
different angles and with different facial expressions.
• Output from system
8/10May 8, 2017 Source of Code: www.mathworks.com
11. Future Scope
• Our current recognition system acquires face images only from files located on
magnetic mediums. Camera and Scanner support can be implemented for greater
flexibility.
• Currently we have not done any work to mark attendance of detected student
which we can do in future.
• Face recognition system used today work very well in constant lighting conditions,
but fails under the vastly varying conditions.
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