Face recognition is a biometric technology that goes beyond just detecting human faces in an image or video. It goes a bit further to determine whose face it is. A face recognition system works by taking an image of a face and predicting whether the face matches another face stored in a dataset (or whether a face in one image matches a face in another). Created By Suman Ahemed Saikan
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
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.
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.
Face recognition for augmented reality and media management.Viewdle.2011.Alexa Dovgopolaya
Applications and building blocks of the face recognition technology developed by Viewdle. Concepts and products of face recognition usage in cell phone augmented reality and photo-video content management and sharing are presented. Overview of the technology building blocks targeting different hardware and software environment is given. Among others face detection, feature detection, face tracking and face recognition operation in different environments and applications is considered. Prototypes of the products are presented.
Face recognition is a biometric technology that goes beyond just detecting human faces in an image or video. It goes a bit further to determine whose face it is. A face recognition system works by taking an image of a face and predicting whether the face matches another face stored in a dataset (or whether a face in one image matches a face in another). Created By Suman Ahemed Saikan
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
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.
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.
Face recognition for augmented reality and media management.Viewdle.2011.Alexa Dovgopolaya
Applications and building blocks of the face recognition technology developed by Viewdle. Concepts and products of face recognition usage in cell phone augmented reality and photo-video content management and sharing are presented. Overview of the technology building blocks targeting different hardware and software environment is given. Among others face detection, feature detection, face tracking and face recognition operation in different environments and applications is considered. Prototypes of the products are presented.
Face Recognition System for Door UnlockingHassan Tariq
This is age of Modern Technology and it's becoming necessity
for everyone. Our project is on one of the most basic
daily life security system. As there was a time, when you
had to open the door by yourself or u needed a key of
some sort or a person for guarding some room.
our project changes that view, as we have automated
that old method. It's user friendly and no human interaction
is needed.Door unlocking to provide essential security to our homes, bank lockers , server rooms , private chambers and offices etc.
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.
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
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.
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.
Face Recognition System for Door UnlockingHassan Tariq
This is age of Modern Technology and it's becoming necessity
for everyone. Our project is on one of the most basic
daily life security system. As there was a time, when you
had to open the door by yourself or u needed a key of
some sort or a person for guarding some room.
our project changes that view, as we have automated
that old method. It's user friendly and no human interaction
is needed.Door unlocking to provide essential security to our homes, bank lockers , server rooms , private chambers and offices etc.
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.
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
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.
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.
Profile Identification through Face Recognitionijtsrd
This project is Profile identification through facial recognition system using machine learning, based on K neighbors algorithm. The K neighbours algorithm has high detection rate and fast processing time. Once the face is detected, feature extraction on the face is performed using histogram of oriented gradients which essentially stores the edges of the face as well as the directionality of those edges. Histogram of oriented gradients is an effective form of feature extraction due its high performance in normalizing local contrast. Lastly, training and classification of the facial databases is done where each unique face in the facial database is a class. We attempt to use this facial recognition system on two sets of databases and will analyse the results and then provide the profile of an individual which is written in the Data base created in Firebase. Mr. B. Ravinder Reddy | V. Akhil | G. Sai Preetham | P. Sai Poojitha ""Profile Identification through Face Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23439.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23439/profile-identification-through-face-recognition/mr-b-ravinder-reddy
MongoDB.local DC 2018: Building Intelligent Apps with MongoDB & Google CloudMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
Building Intelligent Apps with MongoDB and Google Cloud - Jane FineMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
Codemotion Roma 2018 - Alessandro Pozone, Matteo Valoriani
If you think there's been a lot of talk about Augmented Reality and Virtual Reality this year, 2018 is going to blow you away. ARkit, ARCore, HoloLens, Magic Leap, Oculus and many others are working to transform our Reality with new products and services. Apple, Microsoft, Intel, Google and Facebook are approaching AR/VR from different perspectives and technologies: in this session we will try to understand how these different technologies can work together and create a shared multi device experience.
How Augment your Reality: Different perspective on the Reality / Virtuality C...Matteo Valoriani
If you think there's been a lot of talk about Augmented Reality and Virtual Reality this year, 2018 is going to blow you away. Apple with ARkit, Google with ARCore , Microsoft with HoloLens, Facebook with Oculus and many others are working to transform our Reality with new products and services in the not-too-distant future. Therefore Apple, Microsoft, Google and Facebook is approaching AR/VR from different perspectives and in this session we will try to understand how these different technologies work and which best suits the different areas (industry 4.0, tourism, healthcare, ...) .
Intel RealSense Hands-on Lab - Rome
Tips and Tricks from Real Case Studies
Arguments:
- Differences among RealSense Cameras
- Limits and constrains of F200 camera
- Tips & Tricks to improve user experience
- Possible scenarios
- Fast and Simplified way to create RealSense applications: NetSense
La battaglia del touchless: quale è la migliore tecnologia oggi disponibile e come sceglierla.
Mostreremo le caratteristiche di vari device disponibili sul mercato (Kinect2, RealSense, Myo, Leap) e analizzeremo i casi d'uso dei diversi device evidenziandone vantaggi e svantaggi (distanza, precisione, supporto...) e come possono essere combinati tra loro. La sessione si concluderà con alcuni criteri di scelta che devono essere considerati prima di iniziare lo sviluppo e che possono evitare problemi e migliorare il risultato finale.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Face recognition
1. Template designed by
Face Recognition using C#
Matteo Valoriani
mvaloriani@gmail.com
@matteovaloriani
Luigi Oliveto
luigi.oliveto@gmail.com
@luigioliveto
2. Matteo Valoriani
CEO of Fifth Element
Speaker and Consultant
PhD at Politecnico of Milano
Microsoft MVP
Intel Software Innovator
email: mvaloriani@gmail.com
twitter: @MatteoValoriani
linkedin: https://it.linkedin.com/in/matteovaloriani
Nice to Meet You
2
3. Luigi Oliveto
Developer
Co-Speaker
Master of Science at Politecnico of Milano
email: luigi.oliveto@gmail.com
twitter: @LuigiOliveto
linkedin: https://it.linkedin.com/in/luigioliveto
Nice to Meet You
3
4. Face Detection vs Face Recognition vs Face Identification
Face Analysis HomeMade
• OpenCV/Emgu
Face Analysis with Cloud Services
• BetaFace
• Microsoft Face API
Face Analysis with Special Camera
• Kinect
• RealSense
Conclusions
• Common Problems and Limits
Agenda
6. Face detection is a computer technology that
identifies human faces in digital images.
Face Detection
Facial Point DetectionFace Detection/Tracking
7. Face Analysis is a computer technology that
analyze human faces in digital images and
elaborate physical and emotional characteristics.
Face Analysis
Gender/Age/Race AnalysisEmotion Analysis
8. Facial recognition system is a computer
application for automatically identifying or
verifying a person from a digital image or a video
frame from a video source.
Face Recognition
Face Similarity/GroupingFace Verification Face Identification
10. Emgu CV is a cross platform .Net wrapper to the OpenCV image
processing library.
What is EMGU
11. The basic layer (layer 1)
contains function, structure
and enumeration mappings
which directly reflect those in
OpenCV.
The second layer (layer 2)
contains classes that mix in
advantanges from the .NET
world.
EMGU Architecture
12. To start with you need to reference 3 EMGU DLL’s.
• Emgu.CV.dll
• Emgu.CV.UI.dll
• Emgu.Util.dll
using Emgu.CV;
using Emgu.Util;
using Emgu.CV.Structure;
Create a project with EMGU
14. The goal of statistical classification is to use an object's
characteristics to identify which class (or group) it belongs to.
An object's characteristics are also known as feature values and
are typically presented to the machine in a vector called a feature
vector.
Machine Learning Classifier
16. OpenCV/EmguCV uses a type of face
detector called a Haar Cascade.
The Haar Cascade is a classifier (detector)
trained on thousands of human faces.
This training data is stored in an XML file,
and is later used by the classifier during
detection.
It’s the easiest ready to use face detection
method which is supported by
OpenCV/EmguCV and has great results.
Haar Feature-based Cascade Classifier
17. The Fisher Classifier is a linear classifier.
A linear classifier achieves this by
making a classification decision based
on the value of a linear combination of
the characteristics.
The Fisher Classifier
20. SDK for Private Cloud Configuration
WebAPI for Public Cloud
BetaFace APIs
21. General face info:
- faces (positions, sizes, angles)
- face landmarks locations (22 basic, 101 pro)
- cropped face images
- gender, age, ethnicity, smile, glasses, mustache and beard detection
Extended measurements:
- face and facial features shapes description
- hair and skin color
- facial hair detection
- approximate hairstyle shape
- background color and clothes color.
BetaFace - Metadata
22. Following functions supported:
- upload image file or submit image url
- retrieve image and face metadata, including cropped face image
- compare single faces or groups of faces and receive similarity confidence along with match
decision.
- transform face image(s) - generate averages from two or more faces, change face
expression or otherwise modify them.
- add user defined metadata tags, store user-adjusted points and face info.
BetaFace - Metadata
25. 1. Get XML string
2. Generate XSD
• https://devutilsonline.com/xsd-xml/generate-xsd-from-xml
3. Generate C# classes
• XML Schema Definition Tool (Xsd.exe)
Create C# Classes from XML
28. FREE:
Current public API key limits: faces search/recognition requests - no limits; new images - 500 images per day (15000 images per month);
Same image with different set of processing flags counts as new image; images in processing queue - 500; transform requests - no limits.
Freemium: 0 EUR/month 500 IMAGE /day, 0.035 EUR extra
Basic: 199 EUR/month 40000 IMAGE/month 0.025 EUR extra
Premium: 399 EUR/month 100000 IMAGE/month 0.02 EUR extra.
IMAGE – Each new image processed via UploadImage, UploadNewImage_File or UploadNewImage_Url functions.
- uploading the same image with different detection_flags counts as IMAGE.
- uploading the same image with the same set of detection_flags while previous processing results are still in cache does not count as
IMAGE.
- no restrictions on recognize, GetRecognizeInfo or GetImageInfo requests; no restrictions on number of namespaces or their size
If you like to subscribe to one of those plans send email to info@betaface.com with your details for invoice and plan you selected. We will
send you your personal API key.
Current data storage policy: Source images are removed from cache shortly after processing. Faces that have no person/namespace
assigned and corresponding image metadata usually cleaned up after 10 days (face IDs and image IDs will be invalidated).
Licensing: Free VS PRO
30. Face Analysis with Cloud Services
Project Oxford
http://www.projectoxford.ai/
31. Face Detection
Face Recognition
• Face Verification
• Similar Face Searching
• Automatic Face Grouping
• Person Identification
Project Oxford Services
33. 1. Access the Project Oxford Portal https://www.projectoxford.ai, and then click
on the "Sign up" button.
2. Sign in with your Microsoft account, or Sign up for a new Azure subscription if
you don't already have one.
3. Go down the list to select an offered service such as "Face APIs" from the list,
and then click through the various windows in order to make a purchase.
4. Click on the item to view the dashboard, and at the bottom of the page, click
on the 'Manage' button to go to the 'Developer Manage Keys' page.
5. Finally, Copy or regenerate subscription keys in the page.
Get Start
39. Grazie a tutti per la partecipazione
Riceverete il link per il download a slide e demo via email nei
prossimi giorni
Per contattarmi
mvaloriani@gmail.com
luigi.oliveto@gmail.com
Grazie
Editor's Notes
Detects human faces in an image and returns face locations, face landmarks, and optional attributes including head-pose, gender, and age.
Analyzes two faces and determine whether they are from the same person.
Finds similar-looking faces of a specified face from a list of candidate faces.
Divides candidate faces into groups based on face similarity.
Identifies persons from a person group by one or more input faces.