Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference 2018
DOI: 10.1145/3197768.3201527
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Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Abstract: Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to interand within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based … Show more

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Cited by 3 publications
(2 citation statements)
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“…In some cases, emotions cannot be interpreted without context [4]. In video-based emotion recognition, facial expression representation often includes three periods, onset, apex and offset [5,6], as shown in Figure 1. The lengths of the periods differ; the onset and offset periods tend to be shorter than the apex period.…”
Section: Introductionmentioning
confidence: 99%
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“…In some cases, emotions cannot be interpreted without context [4]. In video-based emotion recognition, facial expression representation often includes three periods, onset, apex and offset [5,6], as shown in Figure 1. The lengths of the periods differ; the onset and offset periods tend to be shorter than the apex period.…”
Section: Introductionmentioning
confidence: 99%
“…The duration of each varies, leading to unclear temporal borders. In addition, the appearance of spontaneous expressions leads to the presence of multiple apexes [6].…”
Section: Introductionmentioning
confidence: 99%