StepGAN: Gait Feature Extraction Using Generative Adversarial Networks for Footstep Recognition.

Pressure recordings of footsteps during walking can offer a convenient biometric recognition method for applications in security, forensic analysis, and health monitoring. However, footsteps can exhibit high variability due to a complex interplay of internal and external factors, posing a challenge for recognition systems. To address this issue, this study employed generative adversarial networks with a second discriminator and triplet loss to extract features from high-resolution foot pressure images.