TY  - JOUR
PY  - 2023//
TI  - Perspective on emotion detection for automotive applications: performance evaluation of two emotion AI SDKs
JO  - Proceedings of the Human Factors and Ergonomic Society annual meeting
A1  - Nasir, Mansoor
A1  - Sonawane, Kalyani
A1  - Bekkanti, Nikhitha
A1  - Talamonti, Walter
SP  - 2366
EP  - 2371
VL  - 67
IS  - 1
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2169-5067
UR  - http://dx.doi.org/10.1177/21695067231192630
ID  - ref1
ER  -