Is Facial Recognition Biased at Near-Infrared Spectrum As Well?
Not on the map. Why?
Abstract (arXiv)
Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal performance is obtained for women, dark-skinned people, and older adults. However, these published studies have examined the bias of facial recognition in the visible spectrum (VIS). Factors such as facial makeup, facial hair, skin color, and illumination variation have been attributed to the bias of this technology at the VIS. The near-infrared (NIR) spectrum offers an advantage over the VIS in terms of robustness to factors such as illumination changes, facial makeup, and skin color. Therefore, it is worthwhile to investigate the bias of facial recognition at the near-infrared spectrum (NIR). This first study investigates the bias of the face recognition systems at the NIR spectrum. To this aim, two popular NIR facial image datasets namely, CASIA-Face-Africa and Notre-Dame-NIVL consisting of African and Caucasian subjects, respectively, are used to investigate the bias of facial recognition technology across gender and race. Interestingly, experimental results suggest equitable face recognition performance across gender and race at the NIR spectrum.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 31 Oct 2022.
Builds on
12 of 30 references.
References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
- 2021Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphologycited by 32
- 2022GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detectioncited by 32
- 2020Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groupscited by 24
- 2019Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometricscited by 19
- 2022An Examination of Bias of Facial Analysis based BMI Prediction Modelscited by 10
- 2021CASIA-Face-Africa: A Large-scale African Face Image Databasecited by 9
- 2022Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groupscited by 7
- 2021Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifierscited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.