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

TL;DR

BiometricBlender is a Python tool that generates ultra-high dimensional, multi-class synthetic biometric data with controllable feature properties to aid benchmarking of feature screening methods.

Contribution

The paper introduces BiometricBlender, a novel synthetic data generator that mimics real biometric datasets for benchmarking feature screening techniques.

Findings

Allows control over feature usefulness and intercorrelations

Generates data that imitates key properties of real biometric datasets

Facilitates benchmarking of feature screening methods

Abstract

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.