yearwood-complexemotionprofiling-2020.pdf (2.36 MB)
Complex emotion profiling: An incremental active learning based approach with sparse annotations
journal contribution
posted on 2020-08-11, 00:00 authored by Thuseethan Selvarajah, Sutharshan RajasegararSutharshan Rajasegarar, John YearwoodJohn YearwoodGenerally, in-the-wild emotions are complex in nature. They often occur in combinations of multiple basic emotions, such as fear, happy, disgust, anger, sadness and surprise. Unlike the basic emotions, annotation of complex emotions, such as pain, is a time-consuming and expensive exercise. Moreover, there is an increasing demand for profiling such complex emotions as they are useful in many real-world application domains, such as medical, psychology, security and computer science. The traditional emotion recognition systems require a significant amount of annotated training samples to understand the complex emotions. This limits the direct applicability of those methods for complex emotion detection from images and videos. Therefore, it is important to learn the profile of the in-the-wild complex emotions accurately using limited annotated samples. In this paper, we propose a deep framework to incrementally and actively profile in-the-wild complex emotions, from sparse data. Our approach consists of three major components, namely a pre-processing unit, an optimization unit and an active learning unit. The pre-processing unit removes the variations present in the complex emotion images extracted from an uncontrolled environment. Our novel incremental active learning algorithm along with an optimization unit effectively predicts the complex emotions present in-the-wild. Evaluation using multiple complex emotions benchmark datasets reveals that our proposed approach performs close to the human perception capability in effectively profiling complex emotions. Further, our proposed approach shows a significant performance enhancement, in comparison with the state-of-the-art deep networks and other benchmark complex emotion profiling approaches.
History
Journal
IEEE AccessVolume
8Pagination
147711 - 147727Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Piscataway, N.J.Publisher DOI
Link to full text
eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, The AuthorsUsage metrics
Categories
No categories selectedKeywords
active learningcomplex emotionsemotion recognitionincremental learningsparse dataScience & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringPainGoldTrainingCompoundsFeature extractionBiomedical imagingRECOGNITIONEXPRESSIONS
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC