Face Recognition Bias Assessment through Quality Estimation Models Article Swipe
Luis Lopez Paya
,
Pedro Fernández de Córdoba
,
Angela Sanchez Perez
,
Javier Barrachina
,
Manuel Benavent-Lledó
,
David Mulero-Pérez
,
José García‐Rodríguez
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12224649
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12224649
Recent advances in facial recognition technology have achieved outstanding performance, but unconstrained face recognition remains an ongoing issue. Facial-image-quality-evaluation algorithms evaluate the quality of the input samples, providing crucial information about the accuracy of recognition decisions. By doing so, this can lead to improved results in challenging scenarios. In recent years, significant progress has been made in assessing the quality of facial images. The computation of quality scores has become highly precise and closely correlated with the model results. In this paper, we reviewed and analyzed the existing biases of cutting-edge quality-estimation techniques for face recognition. Our experimentation focused on the quality estimators developed by MagFace, FaceQNet, and SER-FIQ and were evaluated on the CelebA reference dataset. A study of bias in the face-recognition model was conducted by analyzing the quality scores presented in each article. This allowed for an examination of existing biases within both the quality estimators and the face-recognition models.
Related Topics
Concepts
Facial recognition system
Computer science
Quality (philosophy)
Artificial intelligence
Face (sociological concept)
Estimator
Computation
Enhanced Data Rates for GSM Evolution
Pattern recognition (psychology)
Image quality
Machine learning
Quality Score
Three-dimensional face recognition
Quality assessment
Computer vision
Image (mathematics)
Face detection
Statistics
Evaluation methods
Algorithm
Mathematics
Engineering
Reliability engineering
Social science
Metric (unit)
Epistemology
Philosophy
Operations management
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12224649
- https://www.mdpi.com/2079-9292/12/22/4649/pdf?version=1700032751
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388693944
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388693944Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12224649Digital Object Identifier
- Title
-
Face Recognition Bias Assessment through Quality Estimation ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-15Full publication date if available
- Authors
-
Luis Lopez Paya, Pedro Fernández de Córdoba, Angela Sanchez Perez, Javier Barrachina, Manuel Benavent-Lledó, David Mulero-Pérez, José García‐RodríguezList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12224649Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/22/4649/pdf?version=1700032751Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/12/22/4649/pdf?version=1700032751Direct OA link when available
- Concepts
-
Facial recognition system, Computer science, Quality (philosophy), Artificial intelligence, Face (sociological concept), Estimator, Computation, Enhanced Data Rates for GSM Evolution, Pattern recognition (psychology), Image quality, Machine learning, Quality Score, Three-dimensional face recognition, Quality assessment, Computer vision, Image (mathematics), Face detection, Statistics, Evaluation methods, Algorithm, Mathematics, Engineering, Reliability engineering, Social science, Metric (unit), Epistemology, Philosophy, Operations management, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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