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      Spontaneous Pain Expression Recognition in Video Sequences

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      Visions of Computer Science - BCS International Academic Conference (VOCS)

      BCS International Academic Conference

      22 - 24 September 2008

      Spontaneous facial expressions, Pain, Transferable Belief Model, Classification

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          Abstract

          Automatic recognition of Pain expression has potential medical significance. In this paper we present results of the application of an automatic facial expression recognition system on sequences of spontaneous Pain expression. Twenty participants were videotaped while undergoing thermal heat stimulation at nonpainful and painful intensities. Pain was induced experimentally by use of a Peltierbased, computerized thermal stimulator with a 3 × 3 cm 2 contact probe. Our aim is to automatically recognize the videos where Pain was induced. We chose a machine learning approach, previously used successfully to categorize the six basic facial expressions in posed datasets [1, 2] based on the Transferable Belief Model. For this paper, we extended this model to the recognition of sequences of spontaneous Pain expression. The originality of the proposed method is the use of the dynamic information for the recognition of spontaneous Pain expression and the combination of different sensors: facial features behavior, transient features and the context of the expression study. Experimental results show good classification rates for spontaneous Pain sequences especially when we use the contextual information. Moreover the system behaviour compares favourably to the human observer in the other case, which opens promising perspectives for the future development of the proposed system.

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          Most cited references 22

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          Deciphering the enigmatic face: the importance of facial dynamics in interpreting subtle facial expressions.

          Most studies investigating the recognition of facial expressions have focused on static displays of intense expressions. Consequently, researchers may have underestimated the importance of motion in deciphering the subtle expressions that permeate real-life situations. In two experiments, we examined the effect of motion on perception of subtle facial expressions and tested the hypotheses that motion improves affect judgment by (a) providing denser sampling of expressions, (b) providing dynamic information, (c) facilitating configural processing, and (d) enhancing the perception of change. Participants viewed faces depicting subtle facial expressions in four modes (single-static, multi-static, dynamic, and first-last). Experiment 1 demonstrated a robust effect of motion and suggested that this effect was due to the dynamic property of the expression. Experiment 2 showed that the beneficial effect of motion may be due more specifically to its role in perception of change. Together, these experiments demonstrated the importance of motion in identifying subtle facial expressions.
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            Automatic analysis of facial expressions: the state of the art

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              Automatic facial expression analysis: a survey

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                Author and article information

                Contributors
                Conference
                September 2008
                September 2008
                : 191-210
                Affiliations
                Département de psychologie, Université de Montréal, Canada
                Article
                10.14236/ewic/VOCS2008.17
                © Zakia Hammal et al. Published by BCS Learning and Development Ltd. Visions of Computer Science - BCS International Academic Conference

                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/

                Visions of Computer Science - BCS International Academic Conference
                VOCS
                Imperial College, London, UK
                22 - 24 September 2008
                Electronic Workshops in Computing (eWiC)
                BCS International Academic 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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