Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition
Hung Vo, Guee-Sang Lee, Hyung-Jeong Yang, Soo-Hyung Kim
Abstract
Facial Expression Recognition (FER) is a challenging task that improves natural human-computer interaction. This paper focuses on automatic FER on a single in-the-wild (ITW) image. ITW images suffer real problems of pose, direction, and input resolution. In this study, we propose a pyramid with super-resolution (PSR) network architecture to solve the ITW FER task. We also introduce a prior distribution label smoothing (PDLS) loss function that applies the additional prior knowledge of the confusion about each expression in the FER task. Experiments on the three most popular ITW FER datasets showed that our approach outperforms all the state-of-the-art methods.
Topics & Concepts
Pyramid (geometry)Computer scienceArtificial intelligenceSmoothingExpression (computer science)Task (project management)ConfusionComputer visionPattern recognition (psychology)Resolution (logic)MathematicsPsychoanalysisManagementPsychologyEconomicsProgramming languageGeometryAdvanced Image Processing TechniquesSpeech and Audio ProcessingImage and Signal Denoising Methods