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    Computer vision & multimodal· 2015

    FaceNet: A Unified Embedding for Face Recognition and Clustering

    Florian Schroff (first author) · 2015

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    DOI 10.1109/cvpr.2015.7298682arXiv 1503.03832OpenAlex W2096733369

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    Why it matters

    Established practical triplet-loss embedding learning for large-scale face recognition and similarity search.

    Condensed from the expert lists' notes.

    The record

    First released
    2015 (from arXiv 1503.03832)
    Citations
    15,517 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.468 · computed rank 271 · consensus 0.71, must-read 0, citations percentile 0.729 (2010s · vision), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.