Chernoff Faces

Chernoff Faces is a multivariate data visualization technique that represents high-dimensional data points as cartoon-like human faces, where variations in facial features such as eye size, eyebrow slant, and mouth curvature correspond to different variable values, enabling intuitive visual discrimination of complex datasets.

20

Publications

546

Citations

48

Authors

33

Institutions

Publications per year

2017–2026

2

Authors

48

  1. DL
    David L. Huff

    The University of Texas at Austin, United States

    2 publications · 40 citations

    2

    h-index

  2. WC
    William C. Black

    Louisiana State University, United States

    2 publications · 40 citations

    2

    h-index

  3. KN
    Kannan Navaneetham

    University of Botswana, Botswana

    2 publications · 24 citations

    2

    h-index

  4. VM
    Vijay Mahajan

    The University of Texas at Austin, United States

    2 publications · 40 citations

    2

    h-index

  5. HR
    Hans Riedwyl

    University of Bern, Switzerland

    2 publications · 140 citations

    2

    h-index

1–5 of 48

Venues

  1. 2

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  2. Journal of Marketing

    JM · 2 publications · 40 citations

    2

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Systems Biology in Reproductive Medicine

    SBiRM · 1 publications · 11 citations

    1

    h-index

1–5