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      Dynamic Analysis of Automatic Facial Expressions Recognition ‗in the Wild‘ Using Generalized Additive Mixed Models and Significant Zero Crossing of the Derivatives

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      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Emotion, Facial Expression, Automatic Recognition, Generalized Additive Mixed Model, Significant Zero Crossing of the Derivatives

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          Abstract

          The analysis of facial expressions is currently a favoured method of inferring experienced emotion, and consequently significant efforts are currently being 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 evolution of emotions using Generalized Additive Mixed Models (GAMM) and Significant Zero Crossing of the Derivatives (SiZer). The time-series analysis of the emotions 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.

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          Toward machine emotional intelligence: analysis of affective physiological state

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            Smoothing Parameter and Model Selection for General Smooth Models

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              Cosmetic use of botulinum toxin-a affects processing of emotional language.

              How does language reliably evoke emotion, as it does when people read a favorite novel or listen to a skilled orator? Recent evidence suggests that comprehension involves a mental simulation of sentence content that calls on the same neural systems used in literal action, perception, and emotion. In this study, we demonstrated that involuntary facial expression plays a causal role in the processing of emotional language. Subcutaneous injections of botulinum toxin-A (BTX) were used to temporarily paralyze the facial muscle used in frowning. We found that BTX selectively slowed the reading of sentences that described situations that normally require the paralyzed muscle for expressing the emotions evoked by the sentences. This finding demonstrates that peripheral feedback plays a role in language processing, supports facial-feedback theories of emotional cognition, and raises questions about the effects of BTX on cognition and emotional reactivity. We account for the role of facial feedback in language processing by considering neurophysiological mechanisms and reinforcement-learning theory.
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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-8
                Affiliations
                Queen‘s University Belfast Belfast, United Kindgom
                Sensum Belfast, United Kindgom
                Article
                10.14236/ewic/HCI2018.1
                © Dupré et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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