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Can Gender Be Predicted from Near-Infrared Face Images?

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Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6754))

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Abstract

Gender classification based on facial images has received increased attention in the computer vision literature. Previous work on this topic has focused on images acquired in the visible spectrum (VIS). We explore the possibility of predicting gender from face images acquired in the near-infrared spectrum (NIR). In this regard, we address the following two questions: (a) Can gender be predicted from NIR face images; and (b) Can a gender predictor learned using VIS images operate successfully on NIR images and vice-versa? Our experimental results suggest that NIR face images do have some discriminatory information pertaining to gender, although the degree of discrimination is noticeably lower than that of VIS images. Further, the use of an illumination normalization routine may be essential for facilitating cross-spectral gender prediction.

Funding from the Office of Naval Research is gratefully acknowledged.

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Author information

Authors and Affiliations

  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, USA

    Arun Ross & Cunjian Chen

Authors
  1. Arun Ross
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  2. Cunjian Chen
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2011 Springer-Verlag Berlin Heidelberg

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Ross, A., Chen, C. (2011). Can Gender Be Predicted from Near-Infrared Face Images?. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-21596-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21595-7

  • Online ISBN: 978-3-642-21596-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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