Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection
| Autor: | Ines Rieger, Jaspar Pahl, Dominik Seuss |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Optimization problem Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Facial recognition system Machine Learning (cs.LG) 020204 information systems 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering business.industry Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing Class (biology) Task (computing) ComputingMethodologies_PATTERNRECOGNITION Action (philosophy) Gesture recognition Face (geometry) Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | FG |
| DOI: | 10.48550/arxiv.2003.08751 |
| Popis: | Balancing methods for single-label data cannot be applied to multi-label problems as they would also resample the samples with high occurrences. We propose to reformulate this problem as an optimization problem in order to balance multi-label data. We apply this balancing algorithm to training datasets for detecting isolated facial movements, so-called Action Units. Several Action Units can describe combined emotions or physical states such as pain. As datasets in this area are limited and mostly imbalanced, we show how optimized balancing and then augmentation can improve Action Unit detection. At the IEEE Conference on Face and Gesture Recognition 2020, we ranked third in the Affective Behavior Analysis in-the-wild (ABAW) challenge for the Action Unit detection task. Comment: Accepted at the 15th IEEE International Conference on Automatic Face and Gesture Recognition 2020, Workshop "Affect Recognition in-the-wild: Uni/Multi-Modal Analysis & VA-AU-Expression Challenges". arXiv admin note: substantial text overlap with arXiv:2002.03238 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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