# Is Facial Recognition Biased at Near-Infrared Spectrum As Well?

arXiv: https://arxiv.org/abs/2211.00129 · HTML: https://mlsanity.com/paper/2211.00129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Analysis of Gender Inequality In Face Recognition Accuracy](https://mlsanity.com/paper/2002.00065/) (2020)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection](https://mlsanity.com/paper/2207.10246/) (2022)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics](https://mlsanity.com/paper/1912.01842/) (2019)
- [An Examination of Bias of Facial Analysis based BMI Prediction Models](https://mlsanity.com/paper/2204.10262/) (2022)
- [CASIA-Face-Africa: A Large-scale African Face Image Database](https://mlsanity.com/paper/2105.03632/) (2021)
- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers](https://mlsanity.com/paper/2110.04427/) (2021)

## Built on by (most cited first, 1 of 1)

- [Human-Imperceptible Physical Adversarial Attack for NIR Face Recognition Models](https://mlsanity.com/paper/2504.15823/) (2025)

## 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.