University of Cambridge > > Rainbow Group Seminars > Who is Afraid of Non-Universal (Deep Learned) Facial Perception?

Who is Afraid of Non-Universal (Deep Learned) Facial Perception?

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Facial Expression Recognition (FER) has become a popular topic within the hyper-active computer vision community, which has led to the development of a plethora of FER solutions easily accessible to the general public. In most cases, based on deep learned facial expression representations. Such solutions became the backbone of human-based interaction research, being used as means for human behavior analysis, the backbone for interaction-driven models, and one of the most fundamental blocks of proposed cognitive architectures. Most of these important research rely blindly on the objective performance of FER systems, and their capability to categorize a face, in most cases even on a frame-level, into one known and pre-determined emotional category. Once you actually understand how deep-learned FER models actually categorize faces, it is easy to see that trusting on their outputs might bias drastically all of the previously mentioned research areas. These models are trained mostly in a supervised task, where groups of pixels are pushed to compose a specific and pre-determined emotional category. In most cases, these affective labels are deeply connected to the scenario represented by the datasets these models were trained on, which changes drastically the interpretation of their FER results. Similarly to the recent advents on non-universal facial perception, understanding the context in which these models were trained might help to avoid a strong bias in their application on fundamental research, and help us to be more responsible in our claims and findings. The goal of this talk is to discuss the core of the problem of trusting blindly FER systems, and to foster a discussion on the importance of understanding their functioning. In this regard, I will present our most recent research on facial expression perception and hot we can address the biasing of affective categorization based on the non-universal perception theory, and how this can impact in future use of FER technology to other fields.

This talk is part of the Rainbow Group Seminars series.

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