Pith. sign in

REVIEW 1 cited by

EarCapAuth: Biometric Method for Earables Using Capacitive Sensing Eartips

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.04657 v1 pith:LEWOXT55 submitted 2024-11-07 cs.CR

classification cs.CR
keywords earcapauthauthenticationbiometricachievescapacitiveearableseartipsfalse
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded into the soft silicone eartips of two earables. For evaluation, we gathered capactive ear canal measurements from 20 participants in 20 wearing sessions (12 at rest, 8 while walking). A per user classifier trained for authentication achieves an EER of 7.62% and can be tuned to a FAR (False Acceptance Rate) of 1% at FRR (False Rejection Rate) of 16.14%. For identification, EarCapAuth achieves 89.95%. This outperforms some earable biometric principles from related work. Performance under motion slightly decreased to 9.76% EER for authentication and 86.40% accuracy for identification. Enrollment can be performed rapidly with multiple short earpiece insertions and a biometric decision is made every 0.33s. In the future, EarCapAuth could be integrated into high-resolution brain sensing electrode tips.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey of Earable Technology: Trends, Tools, and the Road Ahead

    cs.HC 2025-06 conditional novelty 4.0 of 10

    A structured survey of earable computing research from 2022 to 2025, covering sensing modalities, applications, hardware platforms, datasets, and future directions.

Pith tools