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2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.5089-5097
2018

Details

Autor(en) / Beteiligte
Titel
Ring Loss: Convex Feature Normalization for Face Recognition
Ist Teil von
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.5089-5097
Ort / Verlag
IEEE
Erscheinungsjahr
2018
Link zum Volltext
Quelle
IEEE Xplore
Beschreibungen/Notizen
  • We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.
Sprache
Englisch
Identifikatoren
eISSN: 2575-7075
DOI: 10.1109/CVPR.2018.00534
Titel-ID: cdi_ieee_primary_8578632

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