This document discusses face detection, analysis, and recognition using different techniques. It begins by introducing Matteo Valoriani and Luigi Oliveto. It then discusses doing face analysis at home using OpenCV/EmguCV. It covers using cloud services like Betaface and Microsoft Project Oxford. It also discusses using special cameras like Kinect and RealSense for face analysis. It concludes with discussing common problems and limits of face analysis techniques.
In simple terms, Li-Fi can be thought of as a light-based Wi-Fi. That is, it uses light instead of radio waves to transmit information. And instead of Wi-Fi modems, Li-Fi would use transceiver-fitted LED lamps that can light a room as well as transmit and receive information. Since simple light bulbs are used, there can technically be any number of access points.
This technology uses a part of the electromagnetic spectrum that is still not greatly utilized- The Visible Spectrum. Light is in fact very much part of our lives for millions and millions of years and does not have any major ill effect. Moreover there is 10,000 times more space available in this spectrum and just counting on the bulbs in use, it also multiplies to 10,000 times more availability as an infrastructure, globally.
Lifi Technology presentation best view in powerpoint 2013,16
By Zulafqar Ahmed
Comments below for more slides.
if you want some more and good slides comments below for particular slides
Li-Fi Technology advantages,disadvantages,application,scopeLeo Johnson
Project Done by Nirmal Ram.
This slide gives us a pictorial idea about Li-Fi technology.
have a look.this gives us about advantages and disadvantages,application,scope for Li-Fi in future,working principle of Li-Fi .more projects coming soon.
Li-Fi is nothing but just a new wifi. it is 100 times faster than a wifi and more secure.
New carrier technique for 5G mobile communicationmohamed naeem
in that slide , i have presented the evolution of mobile technology as an introduction , presenting the mobile generations and it's relation to the radio spectrum, also i have focused on the concept of new carrier types and how it will work in the 5th generation.
Wi-Fi stands for Wireless Fidelity. Fidelity: A faithful output.
Generally used to connect devices in wireless mode.
It is a term that refers to IEEE 802.11 communications
Li-Fi stands for Light Fidelity.
Uses light instead of radio waves.
Uses Visible part of electromagnetic spectrum.
Also known as Light based Wi-Fi.
Face Recognition with OpenCV and scikit-learnShiqiao Du
A lightweight implementation of Face Recognition system with Python. OpenCV and scikit-learn.
Python, OpenCv, scikit-learnによる簡易な顔認識システムの実装. Tokyo.Scipy5にて発表。
In simple terms, Li-Fi can be thought of as a light-based Wi-Fi. That is, it uses light instead of radio waves to transmit information. And instead of Wi-Fi modems, Li-Fi would use transceiver-fitted LED lamps that can light a room as well as transmit and receive information. Since simple light bulbs are used, there can technically be any number of access points.
This technology uses a part of the electromagnetic spectrum that is still not greatly utilized- The Visible Spectrum. Light is in fact very much part of our lives for millions and millions of years and does not have any major ill effect. Moreover there is 10,000 times more space available in this spectrum and just counting on the bulbs in use, it also multiplies to 10,000 times more availability as an infrastructure, globally.
Lifi Technology presentation best view in powerpoint 2013,16
By Zulafqar Ahmed
Comments below for more slides.
if you want some more and good slides comments below for particular slides
Li-Fi Technology advantages,disadvantages,application,scopeLeo Johnson
Project Done by Nirmal Ram.
This slide gives us a pictorial idea about Li-Fi technology.
have a look.this gives us about advantages and disadvantages,application,scope for Li-Fi in future,working principle of Li-Fi .more projects coming soon.
Li-Fi is nothing but just a new wifi. it is 100 times faster than a wifi and more secure.
New carrier technique for 5G mobile communicationmohamed naeem
in that slide , i have presented the evolution of mobile technology as an introduction , presenting the mobile generations and it's relation to the radio spectrum, also i have focused on the concept of new carrier types and how it will work in the 5th generation.
Wi-Fi stands for Wireless Fidelity. Fidelity: A faithful output.
Generally used to connect devices in wireless mode.
It is a term that refers to IEEE 802.11 communications
Li-Fi stands for Light Fidelity.
Uses light instead of radio waves.
Uses Visible part of electromagnetic spectrum.
Also known as Light based Wi-Fi.
Face Recognition with OpenCV and scikit-learnShiqiao Du
A lightweight implementation of Face Recognition system with Python. OpenCV and scikit-learn.
Python, OpenCv, scikit-learnによる簡易な顔認識システムの実装. Tokyo.Scipy5にて発表。
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 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
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.
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.
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
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.
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.
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
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.
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.
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/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Face Recognition using C#
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.