# CASIA-Face-Africa: A Large-scale African Face Image Database

arXiv: https://arxiv.org/abs/2105.03632 · HTML: https://mlsanity.com/paper/2105.03632/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 69 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 29 of 29)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Learning from Noisy Labels with Distillation](https://mlsanity.com/paper/1703.02391/) (2017)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces](https://mlsanity.com/paper/1803.09179/) (2018)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020)
- [FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age](https://mlsanity.com/paper/1908.04913/) (2019)
- [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks](https://mlsanity.com/paper/1809.06647/) (2018)
- [Deep Learning for Face Recognition: Pride or Prejudiced?](https://mlsanity.com/paper/1904.01219/) (2019)
- [Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks](https://mlsanity.com/paper/1604.02182/) (2016)
- [Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification](https://mlsanity.com/paper/1709.03654/) (2017)
- [M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis](https://mlsanity.com/paper/1904.00168/) (2019)
- [Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions](https://mlsanity.com/paper/1712.04646/) (2017)

## Built on by (most cited first, 9 of 9)

- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery](https://mlsanity.com/paper/2506.10689/) (2025)
- [Is Facial Recognition Biased at Near-Infrared Spectrum As Well?](https://mlsanity.com/paper/2211.00129/) (2022)
- [Brain-like emergent properties in deep networks: impact of network architecture, datasets and training](https://mlsanity.com/paper/2411.16326/) (2024)
- [VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Security](https://mlsanity.com/paper/2508.07960/) (2025)
- [CASIA-Iris-Africa: A Large-scale African Iris Image Database](https://mlsanity.com/paper/2302.13049/) (2023)
- [Face-MakeUpV2: Facial Consistency Learning for Controllable Text-to-Image Generation](https://mlsanity.com/paper/2510.21775/) (2025)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)

## Abstract (arXiv)

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without re-training are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our http://www.cripacsir.cn/dataset/