# Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study

arXiv: https://arxiv.org/abs/2005.07302 · HTML: https://mlsanity.com/paper/2005.07302/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [MiVOLO: Multi-input Transformer for Age and Gender Estimation](https://mlsanity.com/paper/2307.04616/) (2023)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild](https://mlsanity.com/paper/2106.11145/) (2021)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Towards Fair Face Verification: An In-depth Analysis of Demographic Biases](https://mlsanity.com/paper/2307.10011/) (2023)
- [Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation](https://mlsanity.com/paper/2204.09389/) (2022)
- [SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery](https://mlsanity.com/paper/2108.05465/) (2021)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving](https://mlsanity.com/paper/2312.06306/) (2023)
- [Unified Explanations in Machine Learning Models: A Perturbation Approach](https://mlsanity.com/paper/2405.20200/) (2024)
- [Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning](https://mlsanity.com/paper/2607.14817/) (2026)
- [Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset](https://mlsanity.com/paper/2111.00660/) (2021)
- [Using Synthetic Images To Uncover Population Biases In Facial Landmarks Detection](https://mlsanity.com/paper/2111.01683/) (2021)
- [Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face)](https://mlsanity.com/paper/2506.14909/) (2025)
- [Bias Analysis for Synthetic Face Detection: A Case Study of the Impact of Facial Attributes](https://mlsanity.com/paper/2507.19705/) (2025)
- [A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Dataset](https://mlsanity.com/paper/2609.14145/) (2026)

## Abstract (arXiv)

Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias towards certain demographics. This bias is inflicted both by limited diversity across demographics in the training set, as well as the design of the algorithms. In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification. To this end, we introduce the most comprehensive, large-scale dataset of facial images and videos to date. It consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The data are manually annotated in terms of identity, exact age, gender and kinship. The performance of state-of-the-art models is scrutinized and demographic bias is exposed by conducting a series of experiments. Lastly, a method to debias network embeddings is introduced and tested on the proposed benchmarks.