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Facial Expressions as a Vulnerability in Face Recognition

Citation Author(s):
A. Peña, A. Morales, I. Serna, J. Fierrez, A. Lapedriza
Submitted by:
Alejandro Pena ...
Last updated:
27 September 2021 - 10:15am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Alejandro
Paper Code:
2646
Categories:

Abstract 

Abstract: 

This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases; and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability.

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2021_ICIP_poster.pdf

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