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Facial Expression Recognition

Facial Expression Recognition. By: Stephanie Tsai Nazia Hashmi Michelle Aleong. What is Facial Expression Recognition?. Facial Expression Recognition has been defined as the biometric identification by scanning a person’s face and matching it against a library of faces

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Facial Expression Recognition

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  1. Facial Expression Recognition By: Stephanie Tsai Nazia Hashmi Michelle Aleong

  2. What is Facial Expression Recognition? • Facial Expression Recognition has been defined as the biometric identification by scanning a person’s face and matching it against a library of faces • Process by which the brain and mind understand and interpret the human face

  3. Why Facial Expression? • Behavioral assessment of emotion and paralinguistic displays • Facial nerve disorders  • Computer systems that understand human behavior • Speech recognition. • Security systems. • Lie detection. • Video compression in telecommunications. • Emotion for animation.

  4. FACS • Facial Action Coding System • Most widely used method for measuring and describing facial behaviors • Explains how to categorize facial behaviors based on the muscles that produce them • Goal is to create a reliable means for skilled human scorers to determine the category in which to fit each facial behavior.

  5. Once upon a time… • Developed by Paul Ekman (UCSF) & William Friesen of Langley Porter Neuropsychiatric Institute in San Francisco in 1978 • Current computer programs being developed at the University of Pittsburgh and Carnegie Mellon University, the other by a team at the Salk Institute in La Jolla, California

  6. Action Units Diagram

  7. How it Works • Action Units (AUs) are the measurement units of FACS • 44 AUs • FAC coder “dissects” the expression and decomposes it into the specific AUs that produce the movement

  8. Scoring • The scores consist of the list of AUs that produce it • Descriptive only • AU 1+5+25

  9. Problems with FACS • Human-observer based methods for measuring facial expression are labor intensive, qualitative, and difficult to standardize. • Less than 100% inter observer reliability

  10. Superman (aka. the computer) to the rescue!! • Goal is to make feasible more rigorous, quantitative measurement of facial expression in diverse applications • Computers can recognize specific action units • Unbiased based on person's gender, race or age.

  11. Automated Face Analysis • Automated Face Analysis • Training data on group of more than 200 people of different racial and ethnic backgrounds. • “The hardest and most time-consuming part of all this work is collecting a database of images that is diverse enough and big enough to train the computer," says Sejnowski. • 3-generation system developed at CMU

  12. Generation I

  13. Generation 2

  14. Generation 3

  15. Current Research • Competitions to explore the different methods to analyze the expressions from the same set of videos • Research unit ongoing at CMU Department of Computer Science

  16. Are you ready to have a computer know what you’re feeling?

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