Deformable Facial Model Construction for non-rigid motion tracking, 3D Face Reconstruction Methods, Geometry-Based Methods , Stereo methods, Shape from Motion models, Face Models, Cylindrical Model, Ellipsoidal Model, Planar Model
, Facial deformable models, Holistic models, Part based models, Eigenfaces, Active Shape Models, Combined Appearance Models, comparison of 3D facial features,list of 3d face databases containing 3D static expressions
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์BAINIDA
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTECBAINIDA
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
คณะสถิติประยุกต์ สถาบันบัณฑิตพัฒนบริหารศาสตร์ ร่วมกับ Data Science Thailand ร่วมกันจัดงาน The First NIDA Business Analytics and Data Sciences Contest/Conference
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Boyan Yankov presents the real life business case of VisagiSmile - cutting edge dental software for personalized smile design. The case includes both 2D imaging (facial landmarks, face classification) and 3D modelling (3D teeth model generation) based on data mining techniques and algorithms. After the main talk, Boyan would turn to the audience for ideas for solving the main challenge with the new product - Rebel. Dental - automation of mapping of 3D teeth models.
Eigenfaces , Fisherfaces and Dimensionality_Reductionmostafayounes012
Eigenfaces , Fisherfaces and Dimensionality Reduction are explained easily and clearly. These topics focus on 2 face recognition methods and explains the mathematics behind them. Hope it Helps!
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...TELKOMNIKA JOURNAL
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์BAINIDA
Face recognition and modeling โดย ผศ.ดร.ธนาสัย สุคนธ์พันธุ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTECBAINIDA
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
คณะสถิติประยุกต์ สถาบันบัณฑิตพัฒนบริหารศาสตร์ ร่วมกับ Data Science Thailand ร่วมกันจัดงาน The First NIDA Business Analytics and Data Sciences Contest/Conference
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Boyan Yankov presents the real life business case of VisagiSmile - cutting edge dental software for personalized smile design. The case includes both 2D imaging (facial landmarks, face classification) and 3D modelling (3D teeth model generation) based on data mining techniques and algorithms. After the main talk, Boyan would turn to the audience for ideas for solving the main challenge with the new product - Rebel. Dental - automation of mapping of 3D teeth models.
Eigenfaces , Fisherfaces and Dimensionality_Reductionmostafayounes012
Eigenfaces , Fisherfaces and Dimensionality Reduction are explained easily and clearly. These topics focus on 2 face recognition methods and explains the mathematics behind them. Hope it Helps!
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...TELKOMNIKA JOURNAL
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Reconstruction of partially damaged facial imageeSAT Journals
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Facial Feature Tracking under Varying Facial Expressions and Face Poses based...Yen Ho
This is the 2nd key paper: Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
For faces with expression
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
C program to find factorial of number using recursion as well as iteration ,
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Write a C program to find the first upper case letter in the given string using recursion, write C program to calculate length of the string using Recursion ,
Write a program in C to count number of divisors of a given number using recursion, Recursive program to check whether a given number is prime or composite,
C program to displays integers 100 through 1 using Recursion and Iteration, Write a program in C to convert a decimal number to binary using recursion,
Recursion Stack of factorial of 3 Recursion stack of 4th term of Fibonacci
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
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• How can it help today’s business and the benefits
• Phases in Communication Mining
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
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1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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3. 3D Face Reconstruction Methods
1. Geometry-Based Methods
Assumptions: Faces have symmetrical shape and texture
Input: two face images which are orthogonal to each other
• The frontal photo provides the x and y coordinates .
• Side photo provides the y and z coordinates
• Reconstructed face is texture mapped using the blended texture form the
orthogonal photos.
Advantage:
1. Does not require 3D face database
Disadvantage
1. Depends on quality of the acquisition
2.Fail to produce accurate results for asymmetrical face geometry and
appearance
4. 2. Stereo methods
• Pixel correspondences are established between the two images to create the
disparity map.
• The disparity map and distance between the two cameras are used to
compute the depth map.
Disadvantage
• Performance is often affected by the environment conditions.
3. Shape from Motion models
• Manually annotate 44 facial points on a face as input
• Mark manually same feature points on a generic 3D model.
• Cylindrical projection to map all 3D generic mesh points to 2D
• Triangulate 2D feature points
• Texture map the face onto 3D generic model
• Morph the generic model to match the original.
DISADVANTAGE:
• Requires more source information and the operation is relatively complex.
5. Face Models:
Features of Generic 3D face mesh: Candide v3.1.6 :
• 113 vertices
• All coordinates are between -1.0 to 1.0
• 184 faces/triangles and for each triangle, 3 vertices.
• Each action is implemented as a list of vertex displacements,
describing the change in face geometry.
Advantages:
• Well-defined features
• Efficient Triangulation
Why we require Face Model?
• To interpret images of faces, it is important to have a model of how the
face can appear.
• Changes can be broken down into two parts: changes in shape and
changes in texture (patterns of pixel values) across the face.
6. Cylindrical Model
Advantages:
• Includes both circular and
elliptical cylinder.
• Copes up with large out of plane
rotation
• Robustness to initialization error
• Copes with self occlusions and
pose variations generated by large
head rotations.
• Simple
• Less computational load of a
fitting process
Disadvantages:
• Non-rigid motions cannot be
calculated as the vertices of the
model do not displace.
• Cannot generate actual shape and
texture
7. Ellipsoidal Model
• Ellipsoidal considers horizontally and vertical curved surfaces.
• Accurately captures the 3D motion parameters of the head.
• Is robust to small variations in the initial fit, enabling
the automation of the model initialization.
• It considers the entire 3D aspect of the head, the
tracking is very stable over a large number of frames. This
robustness extends even to sequences with very low frame
rates and noisy camera images.
Planar Model
• Plane model does not represent curved surfaces and is not
robust to out-of-plane rotations.
Figures taken from [12]
8. Facial deformable models
• Holistic models
uses holistic texture based facial representation
Ex: AAM, 3D deformable models
# Discriminative
# Generative
• Part based models
uses local image patches around landmark points
Ex: ASM, CLMs and Tree-based pictorial structures.
Slide Taken from:http://www.robots.ox.ac.uk/~minhhoai/papers/learn2align_CVPR08.pdf
9. Appearance Models
• Eigenfaces (Turk and Pentland, 1991)
– Not robust to shape changes
– Not robust to changes in pose and expression
• Ezzat and Poggio approach (1996)
– Synthesize new views of face from set of example
views
– Does not generalize to unseen faces
Slide taken from: http://www.ai.mit.edu/courses/6.899/lectures/lecture17aam.ppt
10. Active Shape Models
• Point Distribution Model
Training: Apply PCA to labeled images
New image
– Project mean shape
– Iteratively modify model points to fit local neighborhood
Advantages and Disadvantage
• ASM is relatively fast
• ASM too simplistic; not robust when new images
are introduced
• May not converge to good solution
• Key insight: ASM does not incorporate all gray-
level information in parameters
Slide taken from: http://www.ai.mit.edu/courses/6.899/lectures/lecture17aam.ppt
11. Slide Taken from :pages.cpsc.ucalgary.ca/~marina/601/Week6_Face_tracking.ppt
Example of ASM failing
The figure demonstrates the Active Shape Model (ASM) failing. The
main facial features have been found, but the local models searching
for the edges of the face have failed to locate their correct positions,
perhaps because they are too far away. The ASM is a local method and
prone to local minima.
Example of ASM search failure. The search profiles are not long enough to
locate the edges of the face.
12. Combined Appearance Models
• Combine shape and gray-level variation in
single statistical appearance model
• Advantages and Disadvantage
– Inherits appearance model benefits
• Able to represent any face within bounds of the
training set
• Robust interpretation
– Model parameters characterize facial features
Slide taken from: http://www.ai.mit.edu/courses/6.899/lectures/lecture17aam.ppt
13. Parts Based Models
• Each part explains the image data underneath it.
• Model is represented as a graph.
• Vertices represents parts
• Edges represent connection between parts
• If we calculate best location for each part- we get
connections for free.
Deformable model considers each object as a
deformed version of a template leading to compact
representation
16. References
[1]Leung, W., Tseng, B., Shae, Z., Hendriks, F., and Chen, T. 2010. Realistic video avatar. Multimedia and Expo. IEEE.
[2] CANDIDE – a parameterized face. http://www.bk.isy.liu.se/candide/main.html
[3] Narendra Patel, Mukesh Zaveri," 3D Facial Model Construction and Expression Synthesis using a Single Frontal Face
Image”, International Journal of Graphics, November 2010
[4] R. Valenti, N. Sebe, and T. Gevers, "Facial expression recognition: A fully integrated approach," in Int. Workshop on
Visual and Multimedia Digital Libraries, 2007
[5] Iain Matthews, Jing Xiao, Simon Baker. “2D vs 3D Deformable Face Models: Representational Power, Construction
and Real-Time Fitting” International Journal of Computer Vision, Springer 2007.
[6] P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137–154,
2004.
[7]M.Turk and A.Pentland. Eigen faces for recognition. Journal Cognitive Neuroscience, 3(1):71-86,1991
[8] I. Matthews and S.Baker. Active appearance models revisited. International Journal of Computer Vision, 60(2):135-
164, Nov. 2004
[9] Hamimah Ujir, “3D facial expresion classification using a statistical model of surface normals and modular approach,
theisus university of bBirmingham, 2012
[10] K. H. An and M. Chung "3D head tracking and pose-robust 2D texture map-based face recognition using a simple
ellipsoid model", Proc. Intell. Robots Syst., pp.307 -312 2008
[11] Jung, Sung-Uk, and Mark S. Nixon. "On using gait biometrics to enhance face pose estimation." Biometrics: Theory
Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, 2010.
[12] S. Basu , I. A. Essa and A. P. Pentland "Motion regularization for model-based head tracking", International
Conference on Pattern Recognition, 1996
[13] La Cascia, M.; Sclaroff, S.; Athitsos, V., "Fast, reliable head tracking under varying illumination: an approach based
on registration of texture-mapped 3D models," Pattern Analysis and Machine Intelligence, IEEE Transactions on ,
vol.22, no.4, pp.322,336, Apr 2000
Editor's Notes
Dis-map directly from observed images to the underlying causes of those data (facial expressions)
the system can perform the task successfully without it being clear just how the task is being accomplished.
2.GEN - how the hidden variable (the value to be inferred) would generate observed data.
How images(observations) are generated from causes(facial expression) are known..but do not know how causes are inferred from obsevations
capture the variation in collection of face images
each individual face =linear combination of eigen faces
PCA
small collection of weigths for each image and small set of eigen faces
weight determined by projecting each face by projecting each face onto each eigen face.
so each face represented by small set of eigen face weights