Biometric Personal Authentication by One Step Foot Pressure Distribution by Artificial Immune System

Abstract

This paper proposes a biometric personal authentication based on the pressure distribution while one step walking. We extract one step from a walk on mat type load distribution sensor and use it to personal authentication. In this method, features with respect to weight movement and foot shape during walking are employed, and then a classifier is developed aided by of fuzzy inference. Those classifiers trained by artificial immune system. We employed 30 volunteers. For each volunteer, we obtained walk data six times. Then, we evaluated this method by five training data and one test data. The experiment result shows 15.0% EER (Equal Error Rate) and 7.0% FRR (False Rejection Rate) in verification (1:1 collation) and identification (1:N collation), respectively.