The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data

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dc.contributor.authorOrdun, Catherine
dc.contributor.authorRaff, Edward
dc.contributor.authorPurushotham, Sanjay
dc.date.accessioned2021-01-26T18:23:56Z
dc.date.available2021-01-26T18:23:56Z
dc.descriptionPresented at AAAI FSS-20: Artificial Intelligence in Government and Public Sector, Washington, DC, USAen_US
dc.description.abstractWith the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psychology, machine learning, optics, and affective computing. We will review the known factors of thermal vs. RGB imaging for facial emotion recognition. But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition. However, the transition to adopting thermal imagery as a source for any human-centered AI task is not easy and relies on the availability of high fidelity data sources across multiple demographics and thorough validation. This paper takes the reader on a short review of machine learning in thermal FER and the limitations of collecting and developing thermal FER data for AI training. Our motivation is to provide an introductory overview into recent advances for thermal FER and stimulate conversation about the limitations in current datasets.en_US
dc.description.sponsorshipWe thank the three anonymous reviewers for AAAI 2020 for their feedback and comments. We also thank Steve Escaravage from Booz Allen Hamilton for his review of this article. This work is supported by grant CRII (IIS–1948399) from the National Science Foundation.en_US
dc.description.urihttps://arxiv.org/abs/2009.10589en_US
dc.format.extent13 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2blek-cjvs
dc.identifier.citationCatherine Ordun, Edward Raff and Sanjay Purushotham, The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data, https://arxiv.org/abs/2009.10589en_US
dc.identifier.urihttp://hdl.handle.net/11603/20621
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectcomputer vision and pattern recognitionen_US
dc.subjectartificial intelligenceen_US
dc.subjectimage and video processingen_US
dc.titleThe Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Dataen_US
dc.typeTexten_US

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