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Cited by 347

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

LightFace: A Hybrid Deep Face Recognition Framework

Serengil, Sefik Ilkin, Ozpinar, Alper·2020·2020 Innovations in Intelligent Systems and Applications Conference (ASYU)
DOI 10.1109/asyu50717.2020.9259802
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Abstract

Face recognition constitutes a relatively a popular area which has emerged from the rulers of the social media to top universities in the world. Those frontiers and rule makers recently designed deep learning based custom face recognition models. A modern face recognition pipeline consists of four common stages: detecting, alignment, representation and verification. However, face recognition studies mainly mention the representation stage of a pipeline. In this paper, first of all a review face recognition has been done and then the description of the developed lightweight hybrid high performance face recognition framework has been made. Its hybrid feature enables to switch face recognition models among state-of-the-art ones.

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Crossref · 10.1109/asyu50717.2020.9259802

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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  • On the Labeled Faces in the Wild benchmark, Deepface-supported models achieved accuracy scores of 99.65% for FaceNet-512, 99.41% for ArcFace, 99.38% for Dlib, and 98.78% for VGG-Face, whereas human beings could have just 97.53%.

    “According to [44], the supported models FaceNet-512 got 99.65%; ArcFace got 99.41%; Dlib got 99.38% and VGG-Face got 98.78%; accuracy scores on Labeled Faces in the Wild benchmark whereas human beings could have just 97.53%.”

    From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [44]

Citations

Cites

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Research works printed but not linked: 32

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Metadata

Authors
Serengil, S. I., & Ozpinar, A.
Venue
2020 Innovations in Intelligent Systems and Applications Conference (ASYU)
Publisher
IEEE
Published
2020-10-15
Pages
1-5
DOI
10.1109/asyu50717.2020.9259802
Cited by
347
References
1
Licence

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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