Learning Disentangled Expression Representations from Facial Images

• URL: http://arxiv.org/abs/2008.07001v2
• Date: Tue, 18 Aug 2020 06:58:13 GMT
• Title: Learning Disentangled Expression Representations from Facial Images
• Authors: Marah Halawa, Manuel W\"ollhaf, Eduardo Vellasques, Urko S\'anchez Sanz, and Olaf Hellwich
• Abstract summary: We use a formulation of the adversarial loss to learn disentangled representations for face images. The used model facilitates learning on single-task datasets and improves the state-of-the-art in expression recognition with an accuracy of60.53%.
• Score: 2.2509387878255818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face images are subject to many different factors of variation, especially in unconstrained in-the-wild scenarios. For most tasks involving such images, e.g. expression recognition from video streams, having enough labeled data is prohibitively expensive. One common strategy to tackle such a problem is to learn disentangled representations for the different factors of variation of the observed data using adversarial learning. In this paper, we use a formulation of the adversarial loss to learn disentangled representations for face images. The used model facilitates learning on single-task datasets and improves the state-of-the-art in expression recognition with an accuracy of60.53%on the AffectNetdataset, without using any additional data.

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