CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction 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
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
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.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
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.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
Avvkskeve vsjsoneceyeu scgsuieks na scec snsjscsyisbs svegsijsceiebe svsjskndcdidken scegsjjebececgdcr. E ejdidnrceyjevr evhejevr .uwjegejiej.eveibe e e.ejevhej.
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.
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.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
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.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
Avvkskeve vsjsoneceyeu scgsuieks na scec snsjscsyisbs svegsijsceiebe svsjskndcdidken scegsjjebececgdcr. E ejdidnrceyjevr evhejevr .uwjegejiej.eveibe e e.ejevhej.
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.
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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A novel approach for performance parameter estimation of face recognition bas...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
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Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
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This paper describes for a robust face recognition system using skin segmentation technique. This paper addresses the problem of detecting faces in color images in the presence of various lighting conditions. In this paper the face is preprocessed using histogram equalization to avoid illumination problems and then is detected using skin segmentation method. The principal component analysis using neural network is used to recognize the extracted facial features.
This project includes two face recognition systems implemented with the help of Principal Component Analysis (PCA) and Morphological Shared-Weight Neural Network(MSNN).From these systems we will evaluate the performance of both the techniques and based on the accuracy achieved we determine which technique will be better for the face recognition
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
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%.
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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.
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- 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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
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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
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Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
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Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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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:
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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/
3. 2
Introduction
● In the present scenario, there is great need to maintain information security or protection
for physical property
● Information and property can be secured through verification of “true” individual identity
● Unlike other forms of identification such as fingerprinting analysis and iris scans, face
recognition is also user-friendly and non-intrusive.
● It consist of unique shape analysis,patten and positioning of facial features.
Motivation
● Human have the ability to recognize a face with any background condition even after
years of not seeing the face.Therefore, it is the motivation to mimic such a
system,However,it is very challenging task to recognize the face with stuffs like
beard,makeup,covered face,scars etc.
● Numeros studies exploiting various concepts and problems in the face recognition
process.
● Artificial neural networks have been researched heavily in the recent past because of its
similarity to the human brain.
● Our Facial recognition algorithm compares a captured image against a database of stored
faces and tries to match them in extreme conditions.
4. 3
● Background, illumination,angle and other factors make it difficult
● System designed to accurately classify images subject to a variety of unpredictable
conditions.
Overview
The title of this project is “Criminal Detection System”. This application detects the faces from
different angles and run a search through the database for the nearest match and if found, it
displays the matched face. This is done by using neural networks technology.
The face is our primary focus of attention in social life playing an important role in conveying
identity and emotions. We can identify a number of faces learned throughout our lifespan and
identify faces at a glance even after years of separation. This skill is quite robust despite of large
variations in visual stimulus due to changing condition, aging and distractions such as beard,
glasses or changes in hairstyle.
Computational models of face recognition are interesting because they can contribute not only to
theoretical knowledge but also to practical applications. Computers that detect and recognize
faces could be applied to a wide variety of tasks including criminal identification, security
system, image and film processing, identity verification, tagging purposes and human-computer
interaction. Unfortunately, developing a computational model of face detection and recognition
is quite difficult because faces are complex, multidimensional and meaningful visual stimuli.
In our project, we have studied and implemented a pretty simple but very effective face detection
algorithm which takes human skin colour and effective face pattern into account.The individual
can be identified under covered face situation.Our Algorithm mainly focuses on the face
complexity stages and the weighted pattern to recognise the face in hard situation.
Our aim, which we believe we have reached, was to develop a method of face recognition that is
fast, robust, reasonably simple and accurate with a relatively simple and easy to understand
algorithms and techniques.
Technologies Needed
•Software
MATLAB 8.1 (r2015a)
•Hardware
5. 4
USB PC Camera
•Required Products
Image Acquisition Toolbox
Image Processing Toolbox
Computer Vision System Toolbox
Neural Network Toolbox
Methodology
Face Recognition consist of two major tasks-
Locating a face from an image
1. Determine parameter of the image like color,stillness ,etc
2. Check lighting condition and filter it out.
3. Making an image Restoration point of various face parts.
Recognizing the face
1. Ensuring proper capture of the image and filtering the background.
2. Matching the feature with the database
3. Output
2 FACE RECOGNITION
The face recognition algorithms used here are Principal Component Analysis(PCA), Multilinear
Principal Component Analysis (MPCA) and Linear Discriminant Analysis(LDA).Every
algorithm has its own advantage. While PCA is the most simple and fast algorithm, MPCA and
LDA which have been applied together as a single algorithm named MPCALDA provide better
results under complex circumstances like face position, luminance variation etc. Each of them
have been discussed one by one below.
2.1 PRINCIPAL COMPONENT ANALYSIS (PCA)
PCA involves a mathematical procedure that transforms a number of possibly correlated
variables into a number of uncorrelated variables called principal components, related to the
original variables by an orthogonal transformation. This transformation is defined in such a way
that the first principal component has as high a variance as possible (that is, accounts for as much
6. 5
of the variability in the data as possible), and each succeeding component in turn has the highest
variance possible under the constraint that it be orthogonal to the preceding components. PCA is
sensitive to the relative scaling of the original variables. PCA which aims to find the projected
directions along with the minimum reconstructing error and then map the face dataset to a
low-dimensional space spanned by those directions corresponding to the top eigenvalues
Traditional PCA face recognition technology can reach accuracy rate of 70%–92%. However, it
is still not fully practical.
The major advantage of PCA is that the eigenface approach helps reducing the size
of the database required for recognition of a test image. The trained images are not
stored as raw images rather they are stored as their weights which are found out
projecting each and every trained image to the set of eigenfaces obtained.
2.1.1 The eigenface approach
In the language of information theory, the relevant information in a face needs to be extracted,
encoded efficiently and one face encoding is compared with the similarly encoded database. The
trick behind extracting such kind of information is to capture as many variations as possible from
the set of training images.Mathematically, the principal components of the distribution of faces
are found out using the eigenface approach. First the eigenvectors of the covariance matrix of the
set of face images is found out and then they are sorted according to their corresponding
eigenvalues. Then a threshold eigenvalue is taken into account and eigenvectors with
eigenvalues less than that threshold values are discarded. So ultimately the eigenvectors having
the most significant eigenvalues are selected. Then the set of face images are projected into the
significant eigenvectors to obtain a set called eigenfaces. Every face has a contribution to the
eigenfaces obtained. The best M eigenfaces from a M dimensional subspace is called “face
space”[2]
Each individual face can be represented exactly as the linear combination of “eigenfaces” or each
face can also be approximated using those significant eigenfaces obtained using the most
significant eigenvalues.
Now the test image subjected to recognition is also projected to the face space and then the
weights corresponding to each eigenface are found out. Also the weights of all the training
images are found out and stored. Now the weights of the test image is compared to the set of
weights of the training images and the best possible match is found out.
The comparison is done using the “Euclidean distance” measurement. Minimum the distance is
the maximum is the match.
7. 6
Euclidean distance
It is the distance through which we will define or identify the images and also find the matched
image for further neural network recognition.There will be a threshold value for euclidean
distance( .If the comparing value will be less than the threshold,the image will be selected for)λ
neural recognition.
The approach to face recognition involves the following initialisation operations:
1. Fetching an initial set of N face images (training images).
2. Calculate the eigenface from the training set keeping only the M images that correspond to the
highest eigenvalues. These M images define the “facespace”. As new faces are encountered, the
“eigenfaces” can be updated or recalculated accordingly and this will reduce the dimensionality
of image to an efficient number.
3. Calculate the corresponding distribution in M dimensional weight space for each known
individual by projecting their face images onto the “face space”.
4. Calculate a set of weights projecting the input image to the M “eigenfaces”.
5. Determine whether the image is a face or not by checking the closeness of the image to the
“face space” and the capsule neural network.
6. If it is close enough, classify, the weight pattern as either a known person or as an unknown
based on the Euclidean distance measured.
7. If it is close enough then cite the recognition successful and provide relevant information
about the recognised face form the database which contains information about the faces.
2.3 Advantages of PCA
1. It’s the simplest approach which can be used for data compression and face recognition.
2. Operates at a faster rate.
3. Main Feature includes Dimension Reduction,Relevance Removal,Probability Estimation
2.4 Limitations of PCA
1. Requires full frontal display of faces
2. Not sensitive to lighting conditions, position of faces.
3. Considers every face in the database as a different image. Faces of the same person are not
classified in classes.
8. 7
A better approach was studied and used to compensate these limitations which are called
MPCALDA.Its a combination of MPCA and PCA. While MPCA considers the different
variations in images, LDA classifies the images according to same or different person.
2.5 (Multilinear Principal Component Analysis and Linear Discriminant
Analysis )MPCALDA
2.5.1 Multilinear Principal Component Analysis (MPCA)
MPCA is the extension of PCA that uses multilinear algebra and proficient of learning the
interactions of the multiple factors like different viewpoints, different lighting conditions,
different expressions etc.
By Using the MPCALDA approach we want to compared with current traditional existing face
recognition methods, our approach treats face images as multidimensional tensor in order to find
the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project
samples to a new discriminant feature space, while the K nearest neighbor (KNN) is adopted for
sample set classification.[3] In PCA the aim was to reduce the dimensionality of the images. For
example a 20x32x30 dataset was converted to 640x30 that is images are converted to 1D
matrices and then the eigenfaces were found out out of them. But this approach ignores all other
dimensions of an image as an image of size 40 x 32 speaks of a lot of dimensions in a face and
1D vectorizing doesn’t take advantage of all those features. Therefore a dimensionality reduction
technique operating directly on the tensor object rather than its 1D vectorized version will be
applied here.[4]
The approach is similar to PCA in which the features representing a face are reduced by
eigenface approach. While in PCA only one transformation vector was used, in MPCA N
number of different transformation vectors representing the different dimensionality of the face
images are applied.
2.5.2 Linear Discriminant Analysis (LDA)
LDA is a computational scheme for evaluating the significance of different facial attributes in
terms of their discrimination power. The database is divided into a number of classes each class
contains a set of images of the same person in different viewing conditions like different frontal
views, facial expression, different lighting and background conditions and images with or
9. 8
without glasses etc. It is also assumed that all images consist of only the face regions and are of
same size
2.5.3 Face Slicing(FS)
By splitting the data it would become an easier task for face identification. For the construction
of a new image, we can combine any of the facial clippings with any other clipping to construct a
new face. Now based on these newly constructed faces, we can compare these new faces with the
previously saved images in the database and start matching the complete image with the
images which are having some similarities so that we could get the best match from the available
database.
Capsule Network(Network Classifier)
This has three steps--
1. The fed forward of the input training pattern
2. The calculation backpropagation with hidden neural of the associated image slice.Each
image slice will be matched with the database image to ensure the accurate matching og
image.
3. The weighted adjustment.
Neural Networks
● Inspired by biological network of neurons
● The system trains itself initially and keep learning with time and the face use
● Each neuron processes data individually and drives an output
● Layers of neuron form the entire system
● For faster convolution network,we are using improved version of convolution named
capsule network.
● Adjusts weights in training period and real time learning
● Possess incredible ability to recognize pattern of known image under any face cover
condition like beard,makup,enhanced makeup,scars on face etc.
10. 9
Procedure
Sliced Matrix and Training of Images-
•Comparison of sliced images to match the complete image.
•Recursive match computation done to all parts of the image against database
images.
•The classifier identify each and every part of face and train itself for better result.
Thus, we would arrive at a particular image which showcases maximum matches
Image Segmentation
• In Segmentation are using Edge based segmentation technique
• In edge detection technique, the image is split by spotting the difference in pixels
of the digital image or intensity .
• Edge detection technique is determining the value of pixels on the boundaries of
region. The image segmentation is done by edge detection method by noticing
pixels or edges in between diverse section.
Clustering
11. 10
•Clustering is a method in which objects are unified into groups based on their
characteristics.
Uses
All of this makes face recognition ideal for high traffic areas open to the general public, such as:
● Airports and railway stations
● Corporations
● Cashpoints
● Stadiums
● Public transportation
● Financial institutions
● Government offices
● Businesses of all kinds
4 References
[1] M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, Vol.
3, No. 1, Win. 1991, pp. 71-86
[2] MPCA: Multilinear Principal Component Analysis of Tensor Objects, Haiping Lu, Student
Member, IEEE, Konstantinos N. (Kostas) Plataniotis, Senior Member, IEEE, and Anastasios N.
Venetsanopoulos, Fellow, IEEE
[3] Face detection ,Inseong Kim, Joon Hyung Shim, and Jinkyu Yang