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Effect of Parameter Tuning at Distinguishing Between Real and Posed Smiles from Observers’ Physiological Features

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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Abstract

To find the genuineness of a human behavior/emotion is an important research topic in affective and human centered computing. This paper uses a feature level fusion technique of three peripheral physiological features from observers, namely pupillary response (PR), blood volume pulse (BVP), and galvanic skin response (GSR). The observers’ task is to distinguish between real and posed smiles when watching twenty smilers’ videos (half being real smiles and half are posed smiles). A number of temporal features are extracted from the recorded physiological signals after a few processing steps and fused before computing classification performance by k-nearest neighbor (KNN), support vector machine (SVM), and neural network (NN) classifiers. Many factors can affect the results of smile classification, and depend upon the architecture of the classifiers. In this study, we varied the K values of KNN, the scaling factors of SVM, and the numbers of hidden nodes of NN with other parameters unchanged. Our final experimental results from a robust leave-one-everything-out process indicate that parameter tuning is a vital factor to find a high classification accuracy, and that feature level fusion can indicate when more parameter tuning is needed.

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Authors and Affiliations

  1. Research School of Computer Science, Australian National University, Canberra, Australia

    Md Zakir Hossain & Tom Gedeon

Authors
  1. Md Zakir Hossain
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  2. Tom Gedeon
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Corresponding author

Correspondence to Tom Gedeon .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Hossain, M.Z., Gedeon, T. (2017). Effect of Parameter Tuning at Distinguishing Between Real and Posed Smiles from Observers’ Physiological Features. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_89

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_89

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

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