Lightweight face recognition challenge
File(s)
Author(s)
Type
Conference Paper
Abstract
Face representation using Deep Convolutional Neural Network (DCNN) embedding is the method of choice for face recognition. Current state-of-the-art face recognition systems can achieve high accuracy on existing in-the-wild datasets. However, most of these datasets employ quite limited comparisons during the evaluation, which does not simulate a real-world scenario, where extensive comparisons are encountered by a face recognition system. To this end, we propose two large-scale datasets (DeepGlint-Image with 1.8M images and IQIYI-Video with 0.2M videos) and define an extensive comparison metric (trillion-level pairs on the DeepGlint-Image dataset and billion-level pairs on the IQIYI-Video dataset) for an unbiased evaluation of deep face recognition models. To ensure fair comparison during the competition, we define light-model track and large-model track, respectively. Each track has strict constraints on computational complexity and model size. To the best of our knowledge, this is the most comprehensive and unbiased benchmarks for deep face recognition. To facilitate future research, the proposed datasets are released and the online test server is accessible as part of the Lightweight Face Recognition Challenge at the International Conference on Computer Vision, 2019.
Date Issued
2020-03-05
Date Acceptance
2019-10-27
Citation
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2020, pp.2638-2646
ISSN
2473-9936
Publisher
IEEE Computer Society
Start Page
2638
End Page
2646
Journal / Book Title
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Copyright Statement
© 2019 IEEE. This ICCV workshop paper is the Open Access version, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Source
IEEE/CVF International Conference on Computer Vision (ICCV)
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Imaging Science & Photographic Technology
Science & Technology
Technology
Publication Status
Published
Start Date
2019-10-27
Finish Date
2019-11-02
Coverage Spatial
Seoul, South Korea