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Explainable Face Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

Explainable face recognition (XFR) is the problem of explaining the matches returned by a facial matcher, in order to provide insight into why a probe was matched with one identity over another. In this paper, we provide the first comprehensive benchmark and baseline evaluation for XFR. We define a new evaluation protocol called the “inpainting game”, which is a curated set of 3648 triplets (probe, mate, nonmate) of 95 subjects, which differ by synthetically inpainting a chosen facial characteristic like the nose, eyebrows or mouth creating an inpainted nonmate. An XFR algorithm is tasked with generating a network attention map which best explains which regions in a probe image match with a mated image, and not with an inpainted nonmate for each triplet. This provides ground truth for quantifying what image regions contribute to face matching. Finally, we provide a comprehensive benchmark on this dataset comparing five state-of-the-art XFR algorithms on three facial matchers. This benchmark includes two new algorithms called subtree EBP and Density-based Input Sampling for Explanation (DISE) which outperform the state-of-the-art XFR by a wide margin.

Notes

Acknowledgement

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under contract number 2019-19022600003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

Supplementary material

504452_1_En_15_MOESM1_ESM.pdf (15.9 mb)
Supplementary material 1 (pdf 16268 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Systems and Technology ResearchWoburnUSA
  2. 2.Visym LabsCambridgeUSA

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