Simultaneous prediction of valence / arousal and emotion categories and its application in an HRC scenario is a research paper published in Journal of Ambient Intelligence and Humanized Computing (2021). On theSindex it has a DataRank of 0.576. It has been cited 10 times, with 10 citing works in its 1-hop citation network.
AbstractWe address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the AffectNet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arousal values with high accuracies and that the simultaneous learning of discrete categories and continuous values improves the prediction of both. In addition, we use our approach to measure the emotional states of users in an Human-Robot-Collaboration scenario (HRC), show how these emotional states are affected by multiple difficulties that arise for the test subjects, and examine how different feedback mechanisms counteract negative emotions users experience while interacting with a robot system.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.360
From this paper's citation signal
Citation Network Contribution
0.217
From 8 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 62% comes from its base citations and 38% from the citation network (8 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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