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Grant support

Supported by projects: TRESPASS-ETN (MSCA-ITN-2019-860813), PRIMA (MSCA-ITN-2019-860315), BIBECA (RTI2018-101248-B-I00 MINECO/FEDER), and RTI2018-095232-B-C22. A. Pe ~na is supported by a research fellowship from MINECO.

Analysis of institutional authors

Peña, ACorresponding AuthorMorales, AAuthorSerna, IAuthorFierrez, JAuthor
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Proceedings Paper

Facial expressions as a vulnerability in face recognition

Publicated to:Proceedings - International Conference on Image Processing, ICIP. 2021-September 2988-2992 - 2021-01-01 2021-September(), DOI: 10.1109/ICIP42928.2021.9506444

Authors: Pena, A; Morales, A; Serna, I; Fierrez, J; Lapedriza, A

Affiliations

Univ Autonoma Madrid, Biometr & Data Pattern Analyt Lab, Madrid, Spain - Author
Univ Oberta Catalunya, eHlth Ctr, Comp Sci Dept, Barcelona, Spain - Author

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.

Keywords
ExpressionFace recognition

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 8.47, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Apr 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-04-26, the following number of citations:

  • WoS: 9
  • Scopus: 16
  • OpenCitations: 13
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-26:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 25 (PlumX).
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PEÑA ALMANSA, ALEJANDRO) .

the author responsible for correspondence tasks has been PEÑA ALMANSA, ALEJANDRO.