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Exploring Biases in Facial Expression Analysis
Ritik Raina · Miguel Monares · Mingze Xu · Sarah Fabi · Xiaojing Xu · Lehan Li · Will Sumerfield · Jin Gan · Virginia de Sa

Fri Dec 02 07:38 AM -- 07:40 AM (PST) @
Event URL: https://openreview.net/forum?id=jrddoq9NDP6 »

Automated facial expression recognition is useful for many applications, but models are often subject to racial biases. These racial biases may be hard to reveal due to complexity and opacity of the complex networks needed for state-of-the-art performance. Racial biases are also hard to demonstrate due to the inability to fully match facial expressions across real people. In this paper we use artificially created faces where facial expression can be carefully manipulated and matched across artificial faces with different skin colors and different facial shapes. We show that several public facial expression models appear to have a racial bias. In future work, we will work towards using the artificial data to help understand the basis of these biases and remove them from facial expression models.

Author Information

Ritik Raina (University of California, San Diego)
Ritik Raina

Hi! My name is Ritik Raina and I'm a pre-doctoral researcher at UC San Diego where I am advised by Dr. Virginia de Sa. My research interest are in developing cognitively-inspired neural architectures that can be applied to learn robust visual representations.

Miguel Monares (University of California, San Diego)
Mingze Xu
Sarah Fabi (Eberhard-Karls-University Tuebingen)
Xiaojing Xu (UCSD)
Lehan Li (New York University)
Will Sumerfield (University of California, San Diego)
Jin Gan (University of California, San Diego)
Virginia de Sa (University of California, San Diego)

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