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Similarity Measures for Face Images: An Experimental Study

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Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

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Abstract

This work describes experiments aimed at finding a straightforward but effective way of comparing face images. We discuss properties of the basic concepts, such as the Euclidean, cosine and correlation metrics, test the simplest version of elastic templates, and compare these solutions with distances based on texture descriptors (Local Ternary Patterns). The influence of selected image processing methods (e.g. bilateral filtering) on image comparison results is also considered. Additionally, the new metric, in which differences between LTP histograms are weighted with alignment coefficients, is proposed.

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Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland

    Maciej Smiatacz

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  1. Maciej Smiatacz
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Correspondence to Maciej Smiatacz .

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Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

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Smiatacz, M. (2016). Similarity Measures for Face Images: An Experimental Study. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-46418-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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