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Deep Learning Based Age and Gender Recognition Applied to Finger Vein Images

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13645))

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Abstract

Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. It was shown in previous work that biometric data can include more than only identity related information like e.g. age, gender and ethnicity. In this work, deep learning based methods are employed to find out if finger vein image data includes information on the age and gender of the subjects. In our experiments we use different CNNs and different loss functions (triplet loss, SoftMax loss and Mean Squared Error loss) to predict gender and age based on finger vein image data. Using three publicly available finger vein image datasets, we show that it is feasible to predict the gender (accuracies of up to 93.1%). By analyzing finger vein data from different genders we found out that the finger thickness and especially the total length over all finger veins are important features to differentiate between images from male and female subjects. On the other hand, estimating the age of the subjects hardly worked at all in our experiments.

This project was partly funded from the FFG KIRAS project AUTFingerATM under grant No. 864785 and the FWF project “Advanced Methods and Applications for Fingervein Recognition” under grant No. P 32201-NBL.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Jakob-Haringer-Strasse 2, 5020, Salzburg, Austria

    Georg Wimmer, Bernhard Prommegger & Andreas Uhl

Authors
  1. Georg Wimmer
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  2. Bernhard Prommegger
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  3. Andreas Uhl
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Correspondence to Georg Wimmer .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. University of Ontario Institute of Technology, Oshawa, ON, Canada

    Bill Kapralos

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Wimmer, G., Prommegger, B., Uhl, A. (2023). Deep Learning Based Age and Gender Recognition Applied to Finger Vein Images. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_42

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  • DOI: https://doi.org/10.1007/978-3-031-37731-0_42

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