Natália Soares Girão 
, Márcia Muller & Lúcia Valéria Arruda
Abstract: Personal identification through biometric systems recognizes a person’s unique biological or behavioral characteristics. For high-security systems, user identification requires reliable and robust biometric methods. This work develops a method for footstep biometric recognition, based on pressure-sensor signals and convolutional neural networks, for access to restricted areas. The biometric acquisition device is a platform instrumented with fiber Bragg gratings to capture optical signals associated with the plantar pressure distribution. A preprocessing algorithm transforms the gratings’ wavelength shifts in the acquired signals into a plantar pressure map, separating the left and right feet into two smaller images. These small images are used to independently train two convolutional neural networks, one for each foot. After training, both CNNs are used as input to a fully connected neural network (FNN), forming a two-stage neural network (CNN-FNN) model whose weights are trained to identify a person correctly. This CNN-FNN biometric system identifies users from the complete plantar pressure map comprising both feet. The developed system achieves 98.67% accuracy, making it an efficient tool for inclusion in biometric security systems.
Keywords: Footstep Identification, Pressure Map, Fiber Bragg Gratings, Convolutional Neural Network.
DOI code: 10.21528/lnlm-vol24-no1-art5
PDF file: vol24-no1-art5.pdf
BibTex file: vol24-no1-art5.bib