Implementing Behavioral Biometrics with Open-source ML Libraries: A Deep Dive into Gait Recognition

Introduction to Gait Recognition

My team and I stumbled upon the concept of gait recognition last quarter while working on a project for a client who needed a more secure authentication system. We were tasked with exploring alternatives to traditional biometric methods like facial recognition and fingerprint scanning. Gait recognition, which involves identifying individuals based on the way they walk, seemed like a promising area of research. However, we quickly realized that most documentation and tutorials focused on the basics, leaving out the hard parts - like how it breaks and how to fix it.

The Problem with Traditional Biometric Methods

Traditional biometric methods, such as facial recognition and fingerprint scanning, have several drawbacks. They can be intrusive, requiring direct interaction with a device, and are vulnerable to spoofing attacks. Gait recognition, on the other hand, offers a more discreet and secure alternative. But, as we delved deeper into the world of gait recognition, we encountered numerous challenges, from data collection to model training.

Collecting and Preprocessing Gait Data

We started by collecting gait data using a combination of sensors and cameras. However, we soon realized that the quality of the data was crucial in determining the accuracy of our model. We had to preprocess the data to remove noise and normalize it for training. This involved applying techniques like filtering and feature extraction. I was surprised by how much of a difference proper preprocessing made in the performance of our model.

Training a Gait Recognition Model with PyTorch 2.