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Pupil Size as a Biometric Trait

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Biometric Authentication (BIOMET 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8897))

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Abstract

We investigate the possibility of using pupil size as a discriminating feature for eye-based soft biometrics. In experiments carried out in different sessions in two consecutive years, 25 subjects were asked to simply watch the center of a plus sign displayed in the middle of a blank screen. Four primary attributes were exploited, namely left and right pupil sizes and ratio and difference of left and right pupil sizes. Fifteen descriptive statistics were used for each primary attribute, plus two further measures, which produced a total of 62 features. Bayes, Neural Network, Support Vector Machine and Random Forest classifiers were employed to analyze both all the features and selected subsets. The Identification task showed higher classification accuracies (0.6194 ÷ 0.7187) with the selected features, while the Verification task exhibited almost comparable performances (~ 0.97) in the two cases for accuracy, and an increase in sensitivity and a decrease in specificity with the selected features.

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Authors and Affiliations

  1. Dip. di Ingegneria Industriale e dell’Informazione, Università di Pavia, Via Ferrata 5, 27100, Pavia, Italy

    Nahumi Nugrahaningsih & Marco Porta

Authors
  1. Nahumi Nugrahaningsih
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  2. Marco Porta
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Corresponding author

Correspondence to Marco Porta .

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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Dimo Dimov

  3. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

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Nugrahaningsih, N., Porta, M. (2014). Pupil Size as a Biometric Trait. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-13386-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13385-0

  • Online ISBN: 978-3-319-13386-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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