A Multilevel Fusion Approach for Audiovisual Emotion Recognition

Girija Chetty, Michael Wagner, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

This chapter addresses the aspects of facial expression quantification to detect low, medium, and high levels of expressions. It develops an automatic emotion classification technique for recognizing six different facial emotions—anger, disgust, fear, happiness, sadness, and surprise. The authors evaluated two different facial features for this purpose: facial deformation features and marker‐based features for extracting facial expression features. The results show that the sectored volumetric difference function (SVDF/VDF) shape transformation features allow better quantification of facial expressions as compared to marker‐based features. The further plans for this research will be to find better methods to fuse audiovisual information that can model the dynamics of facial expressions and speech. Segmental level acoustic information can be used to trace the emotions at a frame level
Original languageEnglish
Title of host publicationProceedings of Audiovisual Speech Processing 2008
EditorsAmit Konar, Aruna Chakraborty
Place of PublicationAdelaide
PublisherAVISA
Chapter17
Pages115-120
Number of pages6
Volume2008
ISBN (Electronic)9781118910566
ISBN (Print)9781118130667
Publication statusPublished - 2008
EventAudiovisual Speech Processing 2008 - Moreton Island, Australia
Duration: 26 Sep 200829 Sep 2008

Conference

ConferenceAudiovisual Speech Processing 2008
CountryAustralia
CityMoreton Island
Period26/09/0829/09/08

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Chetty, G., Wagner, M., & GOECKE, R. (2008). A Multilevel Fusion Approach for Audiovisual Emotion Recognition. In A. Konar, & A. Chakraborty (Eds.), Proceedings of Audiovisual Speech Processing 2008 (Vol. 2008, pp. 115-120). Adelaide: AVISA.
Chetty, Girija ; Wagner, Michael ; GOECKE, Roland. / A Multilevel Fusion Approach for Audiovisual Emotion Recognition. Proceedings of Audiovisual Speech Processing 2008. editor / Amit Konar ; Aruna Chakraborty. Vol. 2008 Adelaide : AVISA, 2008. pp. 115-120
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author = "Girija Chetty and Michael Wagner and Roland GOECKE",
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editor = "Amit Konar and Aruna Chakraborty",
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Chetty, G, Wagner, M & GOECKE, R 2008, A Multilevel Fusion Approach for Audiovisual Emotion Recognition. in A Konar & A Chakraborty (eds), Proceedings of Audiovisual Speech Processing 2008. vol. 2008, AVISA, Adelaide, pp. 115-120, Audiovisual Speech Processing 2008, Moreton Island, Australia, 26/09/08.

A Multilevel Fusion Approach for Audiovisual Emotion Recognition. / Chetty, Girija; Wagner, Michael; GOECKE, Roland.

Proceedings of Audiovisual Speech Processing 2008. ed. / Amit Konar; Aruna Chakraborty. Vol. 2008 Adelaide : AVISA, 2008. p. 115-120.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - A Multilevel Fusion Approach for Audiovisual Emotion Recognition

AU - Chetty, Girija

AU - Wagner, Michael

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PY - 2008

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N2 - This chapter addresses the aspects of facial expression quantification to detect low, medium, and high levels of expressions. It develops an automatic emotion classification technique for recognizing six different facial emotions—anger, disgust, fear, happiness, sadness, and surprise. The authors evaluated two different facial features for this purpose: facial deformation features and marker‐based features for extracting facial expression features. The results show that the sectored volumetric difference function (SVDF/VDF) shape transformation features allow better quantification of facial expressions as compared to marker‐based features. The further plans for this research will be to find better methods to fuse audiovisual information that can model the dynamics of facial expressions and speech. Segmental level acoustic information can be used to trace the emotions at a frame level

AB - This chapter addresses the aspects of facial expression quantification to detect low, medium, and high levels of expressions. It develops an automatic emotion classification technique for recognizing six different facial emotions—anger, disgust, fear, happiness, sadness, and surprise. The authors evaluated two different facial features for this purpose: facial deformation features and marker‐based features for extracting facial expression features. The results show that the sectored volumetric difference function (SVDF/VDF) shape transformation features allow better quantification of facial expressions as compared to marker‐based features. The further plans for this research will be to find better methods to fuse audiovisual information that can model the dynamics of facial expressions and speech. Segmental level acoustic information can be used to trace the emotions at a frame level

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Chetty G, Wagner M, GOECKE R. A Multilevel Fusion Approach for Audiovisual Emotion Recognition. In Konar A, Chakraborty A, editors, Proceedings of Audiovisual Speech Processing 2008. Vol. 2008. Adelaide: AVISA. 2008. p. 115-120