Automated Detection of Human Emotion

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Presentation transcript:

Automated Detection of Human Emotion Jennifer Lee Quarter 1

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

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) http://graphics.usc.edu/cgit/pdf/papers/ICMI2004-emotionrecog_upload.pdf

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

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

Current Progress Facial tilt and movement test image.

Current Progress

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