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Jennifer Lee Final Automated Detection of Human Emotion.

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Presentation on theme: "Jennifer Lee Final Automated Detection of Human Emotion."— Presentation transcript:

1 Jennifer Lee Final Automated Detection of Human Emotion

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

3 Past Research Very good results Very good results About 80-90% accuracy About 80-90% accuracy Generally have access to high quality cameras Generally have access to high quality cameras Two visual-based processes Two visual-based processes Marker based Marker based Shadow based Shadow based AngerSadnessHappinessNeutral Anger0.840.080.000.08 Sadness0.000.900.000.10 Happiness0.00 0.980.02 Neutral0.000.020.140.84 Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information (2004)

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

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

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