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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 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 • Process by which the brain and mind understand and interpret the human face
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.
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.
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
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
Scoring • The scores consist of the list of AUs that produce it • Descriptive only • AU 1+5+25
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
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.
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
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