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Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

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Abstract

Alzheimer Disease (AD) early detection is considered of high importance for improving the quality of life of patients and their families. Amongst all the different approaches for AD detection, significant work has been focused on emotion analysis through facial expressions, body language or speech. Many studies also use the electroencephalogram in order to capture emotions that patients cannot physically express. Our work introduces an emotion recognition approach using facial expression and EEG signal analysis. A novel dataset was created specifically to remark the autobiographical memory deficits of AD patients. This work uses novel EEG features based on quaternions, facial landmarks and the combination of them. Their performance was evaluated in a comparative study with a state of the art methods that demonstrates the proposed approach.

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Author information

Authors and Affiliations

  1. Kingston University, Kingston upon Thames, England

    Juan Manuel Fernandez Montenegro & Vasileios Argyriou

  2. Imperial College, London, England

    Athanasios Gkelias

Authors
  1. Juan Manuel Fernandez Montenegro
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  2. Athanasios Gkelias
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  3. Vasileios Argyriou
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Correspondence to Juan Manuel Fernandez Montenegro .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Montenegro, J.M.F., Gkelias, A., Argyriou, V. (2017). Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-54407-6_17

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