Mansoor Nasir, Kalyani Sonawane, Nikhitha Bekkanti, Walter Talamonti
{"title":"Perspective on Emotion Detection for Automotive Applications: Performance Evaluation of Two Emotion AI SDKs","authors":"Mansoor Nasir, Kalyani Sonawane, Nikhitha Bekkanti, Walter Talamonti","doi":"10.1177/21695067231192630","DOIUrl":null,"url":null,"abstract":"The work presented herein quantifies the limitations of the technology provided by two prominent suppliers in Emotion AI. Each Software Development Kit (SDK) performance was measured for accuracy using image and video databases. The results indicate that while the SDKs show high accuracy in detecting positive emotions (e.g., Happy), the performance suffered for negative emotions (e.g., Angry) due to missed and false detections. The results were worse for structured video datasets and degraded further when subjects were in naturalistic settings. Although Emotion AI have improved greatly in recent years, the current versions are not reliable enough for automotive applications. The paper provides perspectives on the reasons for subpar performance and guidance for improvement for future emotion estimation software.","PeriodicalId":20673,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","volume":"217 1","pages":"2366 - 2371"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/21695067231192630","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The work presented herein quantifies the limitations of the technology provided by two prominent suppliers in Emotion AI. Each Software Development Kit (SDK) performance was measured for accuracy using image and video databases. The results indicate that while the SDKs show high accuracy in detecting positive emotions (e.g., Happy), the performance suffered for negative emotions (e.g., Angry) due to missed and false detections. The results were worse for structured video datasets and degraded further when subjects were in naturalistic settings. Although Emotion AI have improved greatly in recent years, the current versions are not reliable enough for automotive applications. The paper provides perspectives on the reasons for subpar performance and guidance for improvement for future emotion estimation software.
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