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Summary

Generative adversarial networks improve footstep recognition accuracy by extracting unique features from pressure data, even with varied footwear. This enhances biometric security and forensic analysis.

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Area of Science:

Biometrics
Computer Vision
Machine Learning

Background:

Footstep pressure recordings offer a promising biometric recognition method for security and forensics.
High variability in footstep data due to internal/external factors challenges recognition system accuracy.

Purpose of the Study:

To develop a novel feature extraction method for footstep pressure data using deep-generative models.
To improve the accuracy and generalizability of footstep-based biometric recognition systems.

Main Methods:

Utilized generative adversarial networks (GANs) with a second discriminator and triplet loss for feature extraction from high-resolution foot pressure images.
Mapped footstep data from various footwear conditions to a shared domain using barefoot pressure data.
Employed support vector machine classification for individual verification.

Main Results:

The proposed StepGAN feature extractors significantly improved balanced accuracies from 93.3-95.7% to 96.8-98.0% for 20 individuals.
The model demonstrated improved performance even for users and conditions not present in the training data.
Highlighted the capability of deep-generative models to learn distinctive and generalizable footstep representations.

Conclusions:

Deep-generative models, specifically StepGAN, can effectively learn robust and generalizable footstep representations from pressure data.
The developed method shows significant potential for enhancing the reliability of footstep-based biometric recognition systems.
Further research is recommended to address other sources of variability in footstep data.