1. Automated Detection of Human Emotion
Jennifer Lee
Quarter 1

2. Goal
To be able to identify emotions using a low quality camera (webcam).
Applications
Human-Computer Interaction
Alternate Reality
Product Testing

3. Past Research Very good results Two visual-based processes
About 80-90% accuracy
Generally have access to high quality cameras
Two visual-based processes
Marker based
Shadow based
Anger
Sadness
Happiness
Neutral
0.84
0.08
0.00
0.90
0.10
0.98
0.02
0.14
Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information (2004)

4. Development Python (OpenGL, PIL) Read each image GUI Head Shift
Adjustments
Analyze
Tracking Image
Lighting
Adjustments
Webcam
Identify Markers
Feature
Isolation
Produce Tracking
Image

5. Progress Python webcam integration  Tracking Identification GUI
Basic marker identification 
Basic marker tracking 
Head movement compensation 
Detailed marker tracking 
Identification
Basic Identification 
Learning
GUI

6. Current Progress
Facial tilt and movement test image.

7. Current Progress

8. Problems Success rate lower than desired
Learning will improve this rate.