1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
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
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 detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
This presentation of about Face Recognition. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we don't add any face recognition algorithm in presentation.
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
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 detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
This presentation of about Face Recognition. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we don't add any face recognition algorithm in presentation.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Globally, the presence of biometrics is highly approachable to fix any hurdle and irrelevant input and make a secure and tangible environment. Indeed biometrics helps you tremendously. You can manage everything on your basis to compete in the market. Especially for the attendance services in any organization, office, and building, it is the most important thing to record the presence of someone.
Abstract: Face Recognition appears to be an integral part in human-computer interfaces and eservices. To carry out security and fault tolerance various Image Processing techniques have been incorporated using ‘Curse of Dimensionality’ that refers to Classifying a pattern with high dimensions that requires a large number of training data. A face recognition & Detection system is a computer-driven application for automatically identifying or verifying a person from still or video image. It does that by comparing selected facial features in the live image and a facial database where the system returns a possible list of faces corresponding to training samples from the database. The nodal points are measured creating a numerical code, called a faceprint, representing the face in the database. Relatively many techniques are used. Image processing technique has been implemented using Feature extraction by Gabor Filters and database training data using Neural Networks. Multiscale resolution and matrix sampling is efficiently performed using this technique.
Keywords: Image Processing techniques, Curse of Dimensionality, Faceprint, Feature extraction, Gabor Filters, Neural Networks.
Title: Face Recognition & Detection Using Image Processing
Author: Chandani Sharma
International Journal of Recent Research in Mathematics Computer Science and Information Technology (IJRRMCSIT)
Paper Publications
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Abstract: Face Recognition appears to be an integral part in human-computer interfaces and eservices. To carry out security and fault tolerance various Image Processing techniques have been incorporated using ‘Curse of Dimensionality’ that refers to Classifying a pattern with high dimensions that requires a large number of training data. A face recognition & Detection system is a computer-driven application for automatically identifying or verifying a person from still or video image. It does that by comparing selected facial features in the live image and a facial database where the system returns a possible list of faces corresponding to training samples from the database. The nodal points are measured creating a numerical code, called a faceprint, representing the face in the database. Relatively many techniques are used. Image processing technique has been implemented using Feature extraction by Gabor Filters and database training data using Neural Networks. Multiscale resolution and matrix sampling is efficiently performed using this technique.
Keywords: Image Processing techniques, Curse of Dimensionality, Faceprint, Feature extraction, Gabor Filters, Neural Networks.
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
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Similar to Deep learning on face recognition (use case, development and risk) (20)
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Show drafts
volume_up
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.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Deep learning on face recognition (use case, development and risk)
1. A case of Deep Learning :
Face Recognition
BY : HERMAN KURNADI
2. intro
Face recognition is the problem of identifying and verifying
people in a photograph by their face.
Deep learning methods are able to leverage very large
datasets of faces and learn rich and compact
representations of faces, allowing modern models to first
perform as-well and later to outperform the face recognition
capabilities of humans.
It is a task that is trivially performed by humans, even under
varying light and when faces are changed by age or
obstructed with accessories and facial hair. Nevertheless, it
is remained a challenging computer vision problem for
decades until recently
3. WHY face
recognition
(automation*,not
manually**)
•To restrict access to a resource to
one person, called face
authentication.
•To confirm that the person
matches their ID, called face
verification.
•To assign a name to a face, called
face identification.
**Humans can perform this task very easily.
*hard problem to perform automatically with software, even after 60 or more years of research.
i.e.:, recognition of face images
acquired in an outdoor
environment with changes in
illumination and/or pose remains a
largely unsolved problem. In other
words, current systems are still far
away from the capability of the
human perception system
4. • Locate one or
more faces in the
image and mark
with a bounding
box.
1.Face
Detection.
• Normalize the face
to be consistent with
the database, such
as geometry and
photometrics.
1.Face
Alignment. • features from the
face that can be
used for the
recognition task.
1.Feature
Extraction.
• Perform matching
of the face against
one or more known
faces in a prepared
database
1.Face
Recognition.
5. • uses hand-crafted filters that search for and locate faces in photographs based on a deep
knowledge of the domain.
• Can be very fast and very effective when the filters match, although they can fail dramatically
when they don’t, e.g. making them somewhat fragile.
Feature-based face detection
• learns how to automatically locate and extract faces from the entire image.
• Neural networks fit into this class of methods.
image-based face detection
• consists of a sequence of simple-to-complex face classifiers and has attracted extensive
research efforts.
• has been deployed in many commercial products such as smartphones and digital cameras.
• they often fail to detect faces from different angles, e.g. side view or partially occluded faces.
Detector (cascade)
Face Detection
6. •Face Matching:
• Find the best match for a given face.
•Face Similarity:
• Find faces that are most similar to a given face.
•Face Transformation:
• Generate new faces that are similar to a given face.
•Face Verification.
•A one-to-one mapping of a
given face against a known
identity (e.g. is this the person?).
•Face Identification.
•A one-to-many
mapping for a given
against a database of
known faces (e.g. who is
this person?).
Problem of face recognition : supervised predictive modeling task trained on samples with
inputs & outputs.
7. •based on deep convolutional neural networks
•first major leap forward using deep learning for face recognition,
•accuracy of 97.35% reducing the error of the current state of the art by more than 27%, closely approaching human-level
performance(human standard is 97.53%)
DeepFace
•much like DeepFace, although was expanded in identification and verification tasks by training via contrastive loss.
•Accuracy of 99.15%
The DeepID,
•uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in
previous deep learning approaches.
•To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method
FaceNet
•to collect a very large training dataset and use this to train a very deep CNN model for face recognition that allowed them to achieve then
state-of-the-art results on standard datasets.
•a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop
The VGGFace (Visual Geometry Group,Oxford)
Deep Learning for Face Recognition
8. Academia
•The GaussianFace algorithm developed in 2014 by researchers at Hong Kong University
• facial identification scores of 98.52% compared with the 97.53% achieved by humans.
•weaknesses regarding memory capacity required and calculation times.
Facebook
•in 2014, Facebook announced the launch of its DeepFace program which can determine whether two photographed faces belong to the same
person,
•with an accuracy rate of 97.25%.
Google
•in June 2015, Google went one better with FaceNet, a new recognition system with unrivaled scores: 100% accuracy in the reference test Labeled
Faces in The Wild, and 95% on the YouTube Faces DB.
•Using an artificial neural network and a new algorithm,
•This technology is incorporated into Google Photos and used to sort pictures and automatically tag them based on the people recognized.
•it was quickly followed by the online release of an unofficial open-source version known as OpenFace
Microsoft, IBM and Megvii
• FACE++ tools had high error rates when identifying darker-skin women compared to lighter-skin men.
Amazon
•cloud-based face recognition service named Rekognition to law enforcement agencies.
•The solution could recognize as many as 100 people in a single image and can perform face match against databases containing tens of millions of
faces.
•falsely identified 28 members of US Congress as people arrested for crimes.
Development
9. 1. Security - law enforcement
•to combat crime and terrorism
•is used when issuing identity documents, combined with other biometric technologies such as fingerprints.
•is used at border checks to compare the portrait on a digitized biometric passport with the holder's face.
•Drones combined with aerial cameras offer an interesting combination for facial recognition applied to large areas during mass events
Health
•track a patient's use of medication more accurately
•detect genetic diseases such as DiGeorge syndrome with a success rate of 96.6%
•support pain management procedures.
Marketing and retail
•KYC
•By placing cameras in retail outlets, it is now possible to analyze the behavior of shoppers and improve the customer purchase
•Since 2017, KFC, the American king of fried chicken, and Chinese retail and tech giant Alibaba, have been testing a face recognition
payment solution in Hangzhou, China
Use cases
10. China
• video surveillance network countrywide. 176 million surveillance cameras were in use at the end
of 2017 and 626 million are expected by 2020. Chinese police is working with artificial
intelligence companies such as Yitu, Megvii, SenseTime and CloudWalk
2020 Olympic Games in Tokyo (Japan).
• will be used to identify authorized persons and grant them access automatically enhancing their
experience and safety.
India,
• Aadhaar project is the largest biometric database in the world.
• It already provides a unique digital identity number to 1.2 billion residents.
• Face authentication will be available as an add-on service in fusion mode along with one more
authentication factor like fingerprint, Iris or OTP
Use cases
11. •anti-spoofing mechanisms Make sure the captured image has been done
from a person and not from a photograph (2D), a video screen (2D) or a
(3D), (liveness check or liveness detection) Make sure that facial images
(morphed portraits) of two or more individuals have not been joined into a
reference document such as a passport
1.In Russia, Grigory Bakunov has invented a solution to escape the eyes
permanently watching our movements and confuse face detection devices.
He has developed an algorithm which creates special makeup to fool the
software. However, he has chosen not to bring his product to market after
realizing how easily it could be used by criminals. 2.Forbes announced in an
article from May 2018 that researchers from the University of Toronto
have developped an algorithm to disrupt facial recognition software (aka
privacy filter).In short, a user could apply a filter that modifies specific pixels
an image before putting it on the web. These changes are imperceptible to
human eye, but are very confusing for facial recognition algorithms.
Pro Cons
12. A Gentle Introduction to Deep Learning for Face Recognition by Jason Brownlee on May 31,
2019 in Deep Learning for Computer Vision
https://www.gemalto.com/govt/biometrics/facial-recognition
Source
13. Foto Ini oleh Penulis Tidak Diketahui dilisensikan atas namaCC BY