Andelic, Nicole

Dupre, Damien

McKeown, Gary

Morrison, Gawain

The analysis of facial expressions is currently a favored method of inferring experienced emotion, and consequently significant efforts are currently be-ing made to develop improved facial expression recognition techniques. Among these new techniques, those which allow the automatic recognition of facial expression appear to be most promising. This paper presents a new method of facial expression analysis with a focus on the continuous evolu-tion of emotions using Generalized Additive Mixed Models and Significant Zero Crossing of the Derivative (SiZer). The time-series analysis of the emo-tions experienced by participants watching a series of three different online videos suggests that analysis of facial expressions at the overall level may lead to misinterpretation of the emotional experience whereas non-linear analysis allows the significant expressive sequences to be identified

Queen's University Belfast Research Portal

Assessment of automatic facial expressions recognition "in the wild": a time-series analysis using GAMM and SiZer methods

https://pure.qub.ac.uk/ws/files/141168959/HCII_final_07022018.pdf

Assessment of automatic facial expressions recognition "in the wild": a time-series analysis using GAMM and SiZer methods

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Queen's University Belfast Research Portal