# BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space

arXiv: https://arxiv.org/abs/2206.10747 · HTML: https://mlsanity.com/paper/2206.10747/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [The fused Kolmogorov filter: A nonparametric model-free screening method](https://mlsanity.com/paper/1403.7701/) (2014)
- [Biometric Performance as a Function of Gallery Size](https://mlsanity.com/paper/1906.06272/) (2019)
- [Doubly Robust Sure Screening for Elliptical Copula Regression Model](https://mlsanity.com/paper/1808.08551/) (2018)

## Built on by (most cited first, 3 of 3)

- [Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)](https://mlsanity.com/paper/2305.15793/) (2023)
- [Contrastive Federated Learning with Tabular Data Silos](https://mlsanity.com/paper/2409.06123/) (2024)
- [SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding](https://mlsanity.com/paper/2505.15423/) (2025)

## Abstract (arXiv)

The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common. This paper reports a Python package called BiometricBlender, which is an ultra-high dimensional, multi-class synthetic data generator to benchmark a wide range of feature screening methods. During the data generation process, the overall usefulness and the intercorrelations of blended features can be controlled by the user, thus the synthetic feature space is able to imitate the key properties of a real biometric dataset.