BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
This tool solves the problem of dataset scarcity for researchers in biometrics and feature screening, though it is incremental as it builds on synthetic data generation techniques.
The paper introduces BiometricBlender, a Python package that generates ultra-high dimensional, multi-class synthetic data to address the lack of such datasets for benchmarking feature screening methods in biometrics, allowing user control over feature usefulness and intercorrelations to mimic real biometric datasets.
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.