Boosting Unconstrained Face Recognition with Targeted Style Adversary
While deep face recognition models have demonstrated remarkable performance, they often struggle on the inputs from domains beyond their training data. Recent attempts aim to expand the training set by relying on computationally expensive and inherently challenging image-space augmentation of image...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
14.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | While deep face recognition models have demonstrated remarkable performance,
they often struggle on the inputs from domains beyond their training data.
Recent attempts aim to expand the training set by relying on computationally
expensive and inherently challenging image-space augmentation of image
generation modules. In an orthogonal direction, we present a simple yet
effective method to expand the training data by interpolating between
instance-level feature statistics across labeled and unlabeled sets. Our
method, dubbed Targeted Style Adversary (TSA), is motivated by two
observations: (i) the input domain is reflected in feature statistics, and (ii)
face recognition model performance is influenced by style information. Shifting
towards an unlabeled style implicitly synthesizes challenging training
instances. We devise a recognizability metric to constraint our framework to
preserve the inherent identity-related information of labeled instances. The
efficacy of our method is demonstrated through evaluations on unconstrained
benchmarks, outperforming or being on par with its competitors while offering
nearly a 70\% improvement in training speed and 40\% less memory consumption. |
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DOI: | 10.48550/arxiv.2408.07642 |