This document presents a computer vision system that analyzes facial expressions in real-time to provide live feedback from audience members during presentations or sessions. It outlines the background on emotion recognition techniques, the suggested solution of a facial expression analysis system, the system architecture which includes face detection, feature extraction, feature selection, and classification modules, and discusses related work, the relevance of the project, testing accuracy, and ideas for future work.

1. Live
Feedback
Penguins
FCI-CU
Computer vision
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2. Prepared by
 Moustafa Mohamed Ali
 Yasmin Abobakr
 Mo’men Mohamed
 Radwa Samy
 Tariq Senosy
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3. Outline
 Introduction
 Problem Statement
 Background
 Suggested Solution
 Related work
 Project Relevance
 System Architecture
 Accuracy
 Dataset
 Demo
 Future work
 Questions
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4. Introduction
 Emotions in everyday human communication
 Communication ways :
 by language : 7%
 by paralanguage : 38%
 by facial expression : 55%
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5. Problem Statement
 Getting feedback about a specific session from
attendees during the session.
 We can’t stop the session every 𝑥 minutes and
ask the attendees about their opinion.
 We need to find a way that’s
 Quick
 Effective
 Get good estimates
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6. Background
 Computer Vision Systems
 Emotion Recognition techniques
 Brain signals (EEG)
 Speech
 Facial Expression
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7. Suggested Solution
 A computer vision system that can get
feedback from audience of a session by
detecting their emotions –in real time-
through Facial Expression Analysis.
 Theme :
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8. Related work
 AFFDEX android and web application
 MIT Learning Companion
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9. Value Proposition
 Using such system will be valuable in
 Learning purposes
 Speakers’ presentations
 Ads feedback
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10. Project Relevance
 Software Engineering (Agile, VCS, SDLC)
 Genetic Algorithms
 Machine Learning
 Computer Vision
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11. System Architecture
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Feature
Extraction
Feature Selection
Classification
Face
Detection

13. Face Detection
 Viola Jones Algorithm (OpenCV library)
 It's made for frontal face positions
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14. Feature Extraction
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Methods
Gabor Filter
Convolutional Neural Nets.
Local Binary Pattern
Active Appearance Model
Chosen Method(s)
Gabor Filter

15. Feature Reduction
Methods
Genetic Algorithm
Down sampling
PCA
Adaboost
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Chosen Method(s)
Genetic Algorithm (Tested)
Downsampling (Currently in use)

16. Classification
Methods
SVM (lib-svm)
Neural Networks
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Chosen Method(s)
SVM

17. Accuracy
 Validation 67.95%
 Testing 53.125%
 Factors affecting the accuracy
 Dataset size
 Dataset variation
 Features
 Features normalization
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18. Data set
 With more than 1000 image created from
different datasets we trained our system
 Japanese women database (213 image)
 10k US Adult Faces (10,000 faces)
 Total number of images (1500 images)
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19. Demo
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20. Future Work
 Improve accuracy by trying different
methods for each phase
 Use datasets specified for feedback emotions
 Android version
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22. Thank You 
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