2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-CIF-1.5
Paper Title Choose Settings Carefully: Comparing Action Unit detection at Different Settings Using a Large-Scale Dataset
Authors Mina Bishay, Ahmed Ghoneim, Mohamed Ashraf, Mohammad Mavadati, Affectiva, United States
SessionIMT-CIF-1: Computational Imaging 1
LocationArea J
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we investigate the impact of some of the commonly used settings for (a) preprocessing face images, and (b) classification and training, on Action Unit (AU) detection performance and complexity. We use in our investigation a large-scale dataset, consisting of ~55K videos collected in the wild for participants watching commercial ads. The preprocessing settings include scaling the face to a fixed resolution, changing the color information (RGB to gray-scale), aligning the face, and cropping AU regions, while the classification and training settings include the kind of classifier (multi-label vs. binary) and the amount of data used for training models. To the best of our knowledge, no work had investigated the effect of those settings on AU detection. In our analysis we use CNNs as our baseline classification model.