Biologically significant facial landmarks: How significant are they for gender classification?

Syed Gilani, Faisal Shafait, Ajmal Mian

    Research output: Chapter in Book/Conference paperConference paper

    14 Citations (Scopus)
    184 Downloads (Pure)

    Abstract

    Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.
    Original languageEnglish
    Title of host publication2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages96-103
    Number of pages8
    ISBN (Electronic)9781479921263
    ISBN (Print)9781479921263
    DOIs
    Publication statusPublished - 2013
    Event2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Hobart, TAS, Hobart, Australia
    Duration: 26 Nov 201328 Nov 2013

    Conference

    Conference2013 International Conference on Digital Image Computing
    CountryAustralia
    CityHobart
    Period26/11/1328/11/13

    Fingerprint

    Human computer interaction
    Feature extraction
    Experiments

    Cite this

    Gilani, S., Shafait, F., & Mian, A. (2013). Biologically significant facial landmarks: How significant are they for gender classification? In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 96-103). [10.1109/DICTA.2013.6691488] USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2013.6691488, https://doi.org/10.1109/DICTA.2013.6691488
    Gilani, Syed ; Shafait, Faisal ; Mian, Ajmal. / Biologically significant facial landmarks: How significant are they for gender classification?. 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 96-103
    @inproceedings{bb1340b29bfc448aab0da2894c4547c4,
    title = "Biologically significant facial landmarks: How significant are they for gender classification?",
    abstract = "Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.",
    author = "Syed Gilani and Faisal Shafait and Ajmal Mian",
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    Gilani, S, Shafait, F & Mian, A 2013, Biologically significant facial landmarks: How significant are they for gender classification? in 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)., 10.1109/DICTA.2013.6691488, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 96-103, 2013 International Conference on Digital Image Computing, Hobart, Australia, 26/11/13. https://doi.org/10.1109/DICTA.2013.6691488, https://doi.org/10.1109/DICTA.2013.6691488

    Biologically significant facial landmarks: How significant are they for gender classification? / Gilani, Syed; Shafait, Faisal; Mian, Ajmal.

    2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 96-103 10.1109/DICTA.2013.6691488.

    Research output: Chapter in Book/Conference paperConference paper

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    N2 - Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.

    AB - Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.

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    Gilani S, Shafait F, Mian A. Biologically significant facial landmarks: How significant are they for gender classification? In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 96-103. 10.1109/DICTA.2013.6691488 https://doi.org/10.1109/DICTA.2013.6691488, https://doi.org/10.1109/DICTA.2013.6691488