University of Pittsburgh

Estimating Facial Action Unit Intensity on Larger Training Sets

graduate student
Date: 
Friday, April 5, 2019 - 12:30pm - 1:00pm

Facial expressions are important for people to express themselves and interact with each other in social life. Facial expression analysis helps researchers better understand the underlying emotion, intention, physical pain and psychopathology of the performer. Automatic facial expression analysis is important in affective computing, social robotics, marketing, tutoring, and mental health among other applications. Researchers (Ekman, Friesen, Hager, 2002) (Cohn, Ekman, 2005) broke down facial expressions into different facial action units (AUs), which represent specific actions of one or more facial muscles involved in this process. Ekman and colleagues developed a Facial Action Coding System (FACS) to annotate anatomically-based facial actions (AUs) that can describe nearly all possible facial expressions.Action units may vary in both occurrence and intensity. While most research has emphasized AU occurrence, variation in intensity is critical to emotion communication. Social smiles, for instance, have lower intensity and more rapid onset than felt smiles (Ambadar, Z., et al., 2009).Since previous work on AU intensity used different databases with relatively small sizes, it is possible that the limited number of subject within the database attenuated performances of models (Corneanu, Simón, Cohn, & Guerrero, 2016). Besides, (Girard, J., et al. 2015) suggested that the accuracy of AU occurrence detection increased a lot when training set size got larger.Inspired by this work, we trained different models on both large dataset and small dataset and compared their performances.

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