The document describes a framework for facial expression recognition and removal from 3D face models. The framework involves aligning an expressional 3D face to a generic model, building normal and expression residue spaces through training data, learning the relationship between these spaces using RBF regression to infer expressions, and reconstructing a neutral face by subtracting the inferred expression from the input face. The method is evaluated on the BU-3DFE database and shown to effectively recognize and remove expressions, reconstructing neutral faces.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Abstract: Light Blue Optics’ holographic laser projection technology can be utilised to create a virtual image display which, with a volume enclosing less than 700cc, exhibits a form-factor consistent with integration into a rear-view mirror. By combining the visual accommodation and concomitant reaction time benefits of a head-up display with the ability to present high resolution safety-critical information in a rear-view off-axis configuration with large eyebox, significant potential safety benefits can result.
from light blue optics (LBS)
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Abstract: Light Blue Optics’ holographic laser projection technology can be utilised to create a virtual image display which, with a volume enclosing less than 700cc, exhibits a form-factor consistent with integration into a rear-view mirror. By combining the visual accommodation and concomitant reaction time benefits of a head-up display with the ability to present high resolution safety-critical information in a rear-view off-axis configuration with large eyebox, significant potential safety benefits can result.
from light blue optics (LBS)
A Decomposition-based Approach to Modeling and Understanding Arbitrary ShapesDavid Canino
Modeling and understanding complex non-manifold shapes is a key issue in shape analysis and retrieval. The topological structure of a non-manifold shape can be analyzed through its decomposition into a collection of components with a simpler topology. Here, we consider a representation for arbitrary shapes, that we call Manifold-Connected Decomposition (MC-decomposition), which is based on a unique decomposition of the shape into nearly manifold parts. We present efficient and powerful two-level representations for non-manifold shapes based on the MC-decomposition and on an efficient and compact data structure for encoding the underlying components. We describe a dimension-independent algorithm to generate such decomposition. We also show that the MC-decomposition provides a suitable basis for geometric reasoning and for homology computation on non- manifold shapes. Finally, we present a comparison with existing representations for arbitrary shapes.
Elaich module 5 exercise 5.b - Hands on materialselaich
ELAICH - Educational Linkage Approach in Cultural Heritage.
For more information and presentations, please visit: http://elaich.technion.ac.il/
Hands on materials (2): how can we measure the colours?
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
This project was completed during the Lviv Data Science Summer School 2016 (http://cs.ucu.edu.ua/en/summerschool). The project supervisor - Oleksandr Baiev.
Students developed an application for recognition of facial expressions. Several approaches for emotion recognition, choose the best one and implement mobile application from simple real-time demo to messenger with “auto smileys” were investigated during the project work.
A Decomposition-based Approach to Modeling and Understanding Arbitrary ShapesDavid Canino
Modeling and understanding complex non-manifold shapes is a key issue in shape analysis and retrieval. The topological structure of a non-manifold shape can be analyzed through its decomposition into a collection of components with a simpler topology. Here, we consider a representation for arbitrary shapes, that we call Manifold-Connected Decomposition (MC-decomposition), which is based on a unique decomposition of the shape into nearly manifold parts. We present efficient and powerful two-level representations for non-manifold shapes based on the MC-decomposition and on an efficient and compact data structure for encoding the underlying components. We describe a dimension-independent algorithm to generate such decomposition. We also show that the MC-decomposition provides a suitable basis for geometric reasoning and for homology computation on non- manifold shapes. Finally, we present a comparison with existing representations for arbitrary shapes.
Elaich module 5 exercise 5.b - Hands on materialselaich
ELAICH - Educational Linkage Approach in Cultural Heritage.
For more information and presentations, please visit: http://elaich.technion.ac.il/
Hands on materials (2): how can we measure the colours?
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
This project was completed during the Lviv Data Science Summer School 2016 (http://cs.ucu.edu.ua/en/summerschool). The project supervisor - Oleksandr Baiev.
Students developed an application for recognition of facial expressions. Several approaches for emotion recognition, choose the best one and implement mobile application from simple real-time demo to messenger with “auto smileys” were investigated during the project work.
Deformable Facial Models and 3D Face Reconstruction Methods: A surveyLakshmi Sarvani Videla
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
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2014-member-meeting-scottkrig
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Scott Krig, author of the book "Computer Vision Metrics: Survey, Taxonomy, and Analysis," delivers the presentation "Introduction to Feature Descriptors in Vision: From Haar to SIFT" at the September 2014 Embedded Vision Alliance Member Meeting.
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。
The computation of automorphic forms for a group Gamma is
a major problem in number theory. The only known way to approach the higher rank cases is by computing the action of Hecke operators on the cohomology.
Henceforth, we consider the explicit computation of the cohomology by using cellular complexes. We then explain how the rational elements can be made to act on the complex when it originate from perfect forms. We illustrate the results obtained for the symplectic Sp4(Z) group.
Slides of a report on Machine Learning Seminar Series'11 at Kazan (Volga Region) Federal University. See http://cll.niimm.ksu.ru/cms/main/seminars/mlseminar
Tools for Modeling and Analysis of Non-manifold ShapesDavid Canino
My Official PhD thesis is available at http://www.disi.unige.it/dottorato/THESES/2012-01-CaninoD.pdf
They are slides, that I presented on May 7, 2012, while defending my PhD Thesis in Computer Science under the supervision of Professor Leila De Floriani at the DIBRIS Department (Department of Bioengineering, Computer Science, and Systems Engineering) in Genova, Italy.
In previous works, we have proposed a local dissimilarity map (LDM) in order
to compare images. In this research, we show how the LDM can be applied in the
field of symbol recognition. A global dissimilarity measure (GDM) is obtained
from the LDM. This versatile allow to measure symetric as well as asymetric
similarities. A matcher is derived by summing the values of the LDM. The
obtained matcher is compared to the chamfer matching. Its properties are
related to the human similarity judgement from Tversky results. It is tested
on the grec2005 symbol recognition database. Good to excellent results are
obtained without any knowledge on images, and no pre-processing nor
segmentation involved.
The midpoint method or technique is a “measurement” and as each measurement it has a tolerance, but
worst of all it can be invalid, called Out-of-Control or OoC. The core of all midpoint methods is the accurate
measurement of the difference of the squared distances of two points to the “polar” of their midpoint
with respect to the conic. When this measurement is valid, it also measures the difference of the squared
distances of these points to the conic, although it may be inaccurate, called Out-of-Accuracy or OoA. The
primary condition is the necessary and sufficient condition that a measurement is valid. It is comletely
new and it can be checked ultra fast and before the actual measurement starts. .
Modeling an incremental algorithm, shows that the curve must be subdivided into “piecewise monotonic”
sections, the start point must be optimal, and it explains that the 2D-incremental method can find, locally,
the global Least Square Distance. Locally means that there are at most three candidate points for a given
monotonic direction; therefore the 2D-midpoint method has, locally, at most three measurements.
When all the possible measurements are invalid, the midpoint method cannot be applied, and in that case
the ultra fast “OoC-rule” selects the candidate point. This guarantees, for the first time, a 100% stable,
ultra-fast, berserkless midpoint algorithm, which can be easily transformed to hardware. The new algorithm
is on average (26.5±5)% faster than Mathematica, using the same resolution and tested using 42
different conics. Both programs are completely written in Mathematica and only ContourPlot[] has been
replaced with a module to generate the grid-points, drawn with Mathematica’s
Graphics[Line{gridpoints}] function.
An insightful and simple introduction to Dave McClure's Startup Metrics for Pirates (AARRR).
An overall perspective on the importance of measurements and optimization for startups.
@rafaeldahis
Saímos dos tempos modernos mas os tempos modernos não saíram de nós.
Uma viagem de Ford a Facebook para falar de lean product development.
Uma análise de como ainda pensamos produto como há 100 anos atrás, e o que é diferente nas empresas mais inovadoras da internet atualmente em relação a isto.
Apresentação feita no Papaya Ventures, em Fev/2013, com foco em métricas, growth, e contextualização do framework AARRR (Startup Metrics For Pirates), do Dave McClure.
No que eu pensaria ao invés de esperar meu produto se tornar viral do dia para a noite.
Apresentação feita para alunos de Ciência da Computação na UFRJ, em 16/04/2012.
CarrascoMamata: 10.000 users em 24 horas.Rafael Dahis
CarrascoMamata é um site que foi criado em só 15 dias e teve 10.000 usuários cadastrados em 24 horas.
Essa é a apresentação do meu projeto final da graduação, aonde usei o CarrascoMamata para falar de estratégias de desenvolvimento de produtos (viralidade, lean startup e produtos marcantes).
[ apresentado em Jan-2012 ]
Ferramenta (Widget) para seguir tópicos no Twitter, criada quando isso não era possível oficialmente no website.
Trabalho das disciplinas de Sistemas Distribuídos e Programação Avançada - Engenharia de Computação e Informação na UFRJ (2010.1).
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
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:
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
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.
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.
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/
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
3. Introduction
What is our goal?
Obtain a neutral face 3D model from an expressional face
3D model
How can we achieve this?
Learning how to infer the expression
Subtracting the expression
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4. Motivations
3D Facial expression removal benefits…
Performance of 3D face recognition
Improve 3D gender classification methods
Analyzing complex expressions
Face synthesis
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5. Background
This is probably the first attempt in 3D removal…
Comparing it to 3D face synthesis as its opposite process
Interpolation-based
Muscle-based
Example-based
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7. Alignment
We need to adapt the input to a generic 3D model
Why?
Input faces are irregular and posture-variant
They would be difficult to map
Input = A cloud of points
Generic model = Triangle mesh
How can we obtain a normalized mesh?
Fitting the cloud of points to a generic model
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8. Alignment – 1st step
Landmark-constrained Rigid Adjustment
We adjust the posture of O towards G
Landmarks to constrain the fitting
Iterative Closest Point
Creating pairs between both sets
Original Generic
For each point xi ∈ PO
If xi ∈ LO Model O G
Find corresponding landmark yi ∈ LG Point set PO PG
Else
Find nearest point yi ∈ PG Landmark set LO LG
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9. Alignment – 2nd step
Energy-based Generic Model Adaptation
The generic mesh G is deformed to wrap O
It is a energy minimization problem
First, we have to explain these two energy measures
Eg = Geometric Error
Measures the quality of the wrapping
Es = Smooth Error
Measures the smoothness of the process
4/22/2012 Facial Expression Recognition/Removal 9
10. Alignment – 2nd step
Geometric error is measured:
δ is the weight of landmarks
xi ∈ PO yi ∈ PG
ti denotes the offset of yi and its pair xi
It will be calculated by minimizing the total energy function
Landmarks
Rest of the points
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11. Alignment – 2nd step
Smoth error is measured:
N(i) is the 1-ring neighbor at point i
ti and tj denote the offset of points i and j
Landmarks
Rest of the points
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12. Alignment – 2nd step
The energy function
λ (0 ≤ λ ≤ 1) is used as a tradeoff between the errors
Taking in account both λ and δ, they define:
A tradeoff between time-consuming and accuracy
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13. Alignment – 2nd step
Algorithm:
For each point yi ∈ PG
If yi ∉ LG
Find its nearest point xi ∈ PO
Else
Choose its corresponding point xi ∈ LO
For each yi ∈ PG
Calculate its offset ti by minimizing the energy function: E(λ,δ)
Update the point: yi = yi + ti
Compute the total root mean squared distance εk between PO and PG
If εk < threshold
Start again reducing value of λ and δ M is the
Else aligned
Obtain aligned 3D face: M = O 3D model
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14. Training – Building spaces
Normal Space
Properties of facial
expressions
Expression Residue Space
Expression variations
compared with their neutral
faces
Each point on the spaces
stores one face sample
4/22/2012 Facial Expression Recognition/Removal 14
15. Training – Building spaces
Normal space
T represents the triangle set of M
n = (nx,ny,nz) is a normal vector
nj is the normal of a jth triangle on M
C represents the normal space
Is composed by all the normal vectors on T
4/22/2012 Facial Expression Recognition/Removal 15
16. Training – Building spaces
Expression residue space
How a facial expression is understood?
The difference between the expressional face and the neutral face
Δ(Mexpresional ,Mneutral)
This is stored as a combination of movements over each triangle on a
neutral face model
How each movement is encoded?
5-tuple:
azimuth angle
elevation angle
x translation
y translation
z translation
4/22/2012 Facial Expression Recognition/Removal 16
17. Training – Relationship model
We want to be able to:
Infer the expression given a expressional face
In order to do that we need:
A Relationship Model that maps normal space and
expression residue space.
This process is not trivial:
Dimension reduction of Normal Space
Inferring Expression Residue
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18. Training – Relationship model
Dimension reduction of Normal Space
Normal Space contains redundant and noisy information
We will use Principal Component Analysis
ui represents the vector of the ith training sample
Cj represents the jth centralized geodesical coordinate
u1 u2 … uN S matrix
…
C1 C1 C1
C2 C2 C2
C3 C3 C3
U matrix
… … …
CK CK CK Covariance matrix
KxN
KxK
4/22/2012 Facial Expression Recognition/Removal 18
19. Training – Relationship model
Dimension reduction of Normal Space
Once we have the covariance matrix we perform Singular Value
Decomposition (SVD) to obtain:
Eigenvectors (v1, …, vN)
Eigenvalues (λ1, …, λN), sorted from highest to lowest
Selecting the most relevant eigenvectors
P is the set of eigenvectors selected (v1, …, vV)
ξ is a predefined threshold to avoid selecting too many eigenvectors
Finally, we get the reduced normal space
4/22/2012 Facial Expression Recognition/Removal 19
20. Training – Relationship model
Inference of Expressional Residue
RBF regression stands for Radial Basis Functions
They depend only on the distance from a point to the
center
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21. Training – Relationship model
Inference of Expressional Residue
RBF Networks use radial basis functions as activation
functions
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22. Training – Relationship model
Inference of Expressional Residue
RBF(1)
C1 sum(1) e1
C2 RBF(2)
sum(2) e2
sum(k) ek
Cn RBF(n)
Inputs: centralized geodesical coordinates of reduced normal space.
uiP = (C1, C2, …, Cn)
The intermediate nodes compute a RBF that relate Ci to its neighborhood
Outputs: value for each dimension of the expression space
The weights matrix will be computed by least squares method
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23. Testing
Given an expressional face…
Infer the expression residue
Subtract the expression residue
Reconstruct the face
Obtaing the neutral face
Mathematical expression
Mneu = Mexp – Δ(Mexp ,Mneu) M is the
aligned
3D model
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24. Testing - Infering
Being Cexp the normal representation of Mexp
Let Φ(Cexp) be the result of RBF network to the new input
Cexp
Φ(Cexp) is the inference of Δ(Mexp ,Mneu)
Δ(Mexp ,Mneu) ≅ Φ(Cexp)
Final mathematical expression
Mneu = Mexp – Φ(Cexp)
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25. Testing - Reconstruction
Having inferred the expression residue:
We have a set of movements for each triangle on Mexp
Applying them causes the mesh to be deformed
Poisson-based reconstruction
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26. Experiments
BU-3DFED (Binghamton University 3D Facial Expression
Database)
44 males 56 females
Each made 6 different expressions and 1 neutral face
Each expression had 4 levels of intensity
Total number of face models = 700
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27. Experiments
The RMS (root mean square) is used to measure the
performance between the two neutral face models
Xi is a point on X and Yi is a point of Y which is the nearest
to Xi
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34. Introduction
Expressions are dynamic
Easier to recognize them by video than static images
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35. Haar-like features
Our “experts” from face detection
Binary patterns that are convoluted with the images
producing a single value result
Each frame has many important haar-features
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36. Clustering Temporal Patterns
5 stages of an expression will be considered
A clustering method will be used to classify the haar-
features into the 5 stages
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37. Clustering Temporal Patterns
K-Means
N → number of clusters
N random vectors will be initialized, representing the center of
the clusters
For each point in the database:
Which is the closest vector to me?
That's the cluster I belong to!
Recalculate cluster descriptor vectors: they must represent the
mass-center of the points in the cluster
Repeat until there's no more changes
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39. Building our Experts
For representation purpose, a five-dimension vector is
used for each haar-feature
*0 0 0 1 0+ → the haar-feature belongs to the forth stage
(middle+)
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40. Building our Experts
A normalized histogram is calculated, considering all the
features in the sequence
Ex for 7 features: [0 0 1/7 2/7 4/7]
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41. Building our Experts
We will convert the binary vector to decimal
[ 0/7 0/7 1/7 2/7 4/7 ]
= [ 1 2 4 8 16 ]
= 0 + 0 + 4/7 + 16/7 + 64/7 = 84/7 = 12
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42. Building our Experts
An one-against-all approach is used
“Is it a happy expression or not?”
Other moods will work as negative examples
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43. Building our Experts
After repeating the clustering and summarizing process for all
examples in database, we can produce a histogram of YES/NO
to each expressions
A threshold will define if a face represent that expression or
not
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44. Building our Experts
That is one weak classifier
The final strong classifier is build by Adaboost
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45. Testing
For a new sequence:
Calculate the haar-features
Cluster into stages
Summarize (output a decimal)
Compare this value with the threshold of each expression
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46. Experiments
Cohn-Kanade faces database
100 students, from 18 to 30
65% woman, 35% man
15% african-american, 5% asian or latin
Each performed 23 poses, including prototypical expressions
In this work, they used 90 of those expressions (60 for training,
30 for testing)
Experiments were made with sequences of 7 and 9 frames
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51. Poisson-based reconstruction
We paste all the triangles together solving:
AU = b
Being:
U the coordinates of the deformed mesh
b the divergence of the gradient fields modified
A a sparse matrix defined as:
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