Disguised Face Verification Using Inverse Disguise Quality
23 Aug 2020-pp 524-540
TL;DR: In this article, a novel Inverse Disguise Quality metric is proposed for evaluating amount of disguise in the input image, which is utilized in likelihood ratio as a quality score for enhanced verification performance.
Abstract: Research in face recognition has evolved over the past few decades. With initial research focusing heavily on constrained images, recent research has focused more on unconstrained images captured in-the-wild settings. Faces captured in unconstrained settings with disguise accessories persist to be a challenge for automated face verification. To this effect, this research proposes a novel deep learning framework for disguised face verification. A novel Inverse Disguise Quality metric is proposed for evaluating amount of disguise in the input image, which is utilized in likelihood ratio as a quality score for enhanced verification performance. The proposed framework is model-agnostic and can be applied in conjunction with existing state-of-the-art face verification models for obtaining improved performance. Experiments have been performed on the Disguised Faces in Wild (DFW) 2018 and DFW 2019 datasets, with three state-of-the-art deep learning models, where it demonstrates substantial improvement compared to the base model.
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123,388 citations
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14,299 citations