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Matthias Wimmer, Ursula Zucker and Bernd Radig Chair for Image Understanding Computer Science Technische Universität München { wimmerm, zucker, radig }@in.tum.de Human Capabilities on Video-based Facial Expression Recognition
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2007-09-10 2/10 Technische Universität München Ursula Zucker Motivation Facial Expression Recognition goal: human-like man-machine communication six universal facial expressions [Ekman]: anger, disgust, fear, happiness, sadness, surprise minimal muscle activity -> reliable recognition is difficult recognition rate of state-of-the-art approaches: ~ 70% Question How reliable do humans specify facial expressions? -> survey to determine human capabilities
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2007-09-10 3/10 Technische Universität München Ursula Zucker The Facial Expression Database Cohn-Kanade AU-Coded Facial Expression Database 488 image sequences (containing 4 up to 66 images) each showing one of the six universal facial expressions no natural facial expressions (simulated ground truth) no context information
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2007-09-10 4/10 Technische Universität München Ursula Zucker Description of Our Survey Execution of the Survey participants are shown randomly selected sequences 250 participants 5413 annotations -> approx. 11 per sequence
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2007-09-10 5/10 Technische Universität München Ursula Zucker Evaluation Evaluation of the Survey no ground truth -> comparison of the annotations to one another annotation rate for each sequence and each facial expression relative agreement for an expression confusion between facial expressions Comparison to algorithms recognition rate
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2007-09-10 6/10 Technische Universität München Ursula Zucker Annotation Rate for Each Sequence Explanation: 488 rows 1 row = 1 sequence darker regions denote a higher annotation rate sorted by similar annotation Result: happiness: best annotation rates surprise and fear: get confused often fear: difficult to tell apart
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2007-09-10 7/10 Technische Universität München Ursula Zucker Relative Agreement Explanation: example: annotating the sequences as happiness ~ 350 sequences annotated as happiness by nobody, ~ 50 sequences annotated as happiness by everybody well-recognized facial expressions have peaks at “0” and at “1”
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2007-09-10 8/10 Technische Universität München Ursula Zucker Confusion Between Facial Expressions fear and surprise: high confusion happiness and disgust: low confusion confusion rate
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2007-09-10 9/10 Technische Universität München Ursula Zucker Comparison: humans vs. algorithms ground truth: provided by Michel et. al. Results: Michel et. al.: worse at recognizing anger Schweiger et. al.: worse at recognizing disgust, fear, happiness and on the average
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2007-09-10 10/10 Technische Universität München Ursula Zucker Conclusion Survey applies similar assumptions as algorithms: consideration of visual information only no context information no natural facial expressions Summary of our results: poor recognition rate of humans – worse than expected some facial expressions get confused easily Conclusion & Outlook: integration of more sources of information is highly recommended, e. g. audio/language, context,...
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2007-09-10 11/10 Technische Universität München Ursula Zucker Thank you
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