DaliID: Distortion-Adaptive Learned Invariance for Identification Models
- URL: http://arxiv.org/abs/2302.05753v1
- Date: Sat, 11 Feb 2023 18:19:41 GMT
- Title: DaliID: Distortion-Adaptive Learned Invariance for Identification Models
- Authors: Wes Robbins, Gabriel Bertocco, Terrance E. Boult
- Abstract summary: We propose a methodology called Distortion-Adaptive Learned Invariance for Identification (DaliID) models.
DaliID models achieve state-of-the-art (SOTA) for both face recognition and person re-identification on seven benchmark datasets.
- Score: 9.502663556403622
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In unconstrained scenarios, face recognition and person re-identification are
subject to distortions such as motion blur, atmospheric turbulence, or
upsampling artifacts. To improve robustness in these scenarios, we propose a
methodology called Distortion-Adaptive Learned Invariance for Identification
(DaliID) models. We contend that distortion augmentations, which degrade image
quality, can be successfully leveraged to a greater degree than has been shown
in the literature. Aided by an adaptive weighting schedule, a novel distortion
augmentation is applied at severe levels during training. This training
strategy increases feature-level invariance to distortions and decreases domain
shift to unconstrained scenarios. At inference, we use a magnitude-weighted
fusion of features from parallel models to retain robustness across the range
of images. DaliID models achieve state-of-the-art (SOTA) for both face
recognition and person re-identification on seven benchmark datasets, including
IJB-S, TinyFace, DeepChange, and MSMT17. Additionally, we provide recaptured
evaluation data at a distance of 750+ meters and further validate on real
long-distance face imagery.
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