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.
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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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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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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