A Heuristic to Select the Optimal Transformation Matrixes in Bioconvolving with Mixing Transform

EasyChair Preprint no. 10042

Abstract

Bioconvolving with Mixing transform is a cancelable biometric approach to protect biometric data and the user's privacy. This approach uses linear convolutions on biometric features to generate cancelable templates following the random transformation matrixes. This paper shows how the choice of the transformation matrixes impacts the protected system accuracy. Therefore, the random matrix selection is not an optimal strategy. A heuristic algorithm is proposed to select the optimal transformation matrix that achieves the optimal protected system performance. The proposed heuristic is based on the minimum distance between the transformed mean template created by the EB-DBA and the transformed reference set. Two online signature verification systems have been protected by Bioconvonlving with Mixing transform to evaluate the proposed algorithm performance in terms of accuracy, False Negative Rate (FNR), and False Positive Rate (FPR). The experiments have been conducted on SVC2004, xLongSignDb, SUSig VisualSubCorpus, and SUSig BlindSubCorpus online signature datasets. The highest calculated Pearson index (r=0.87) shows a high correlation between the proposed heuristic and the system's accuracy. Therefore, the selected matrixes by the proposed heuristic allow for optimal system performance. The protected system accuracy improved to 11% using the selected transformation matrixes by the proposed heuristic compared to the random selection matrixes. Moreover, protecting the system using Bioconvolving, revised with the proposed heuristic, reduces accuracy at best by only 0.6 % compared to the unprotected system.

Keyphrases: Bioconvolving, Biometric, Cancelable Biometric, Linear Convolution, online signature, template protection

@Booklet{EasyChair:10042,
  author = {Francesco Castro and Stefano Galantucci and Donato Impedovo and Giuseppe Pirlo},
  title = {A Heuristic to Select the Optimal Transformation Matrixes in Bioconvolving with Mixing Transform},
  howpublished = {EasyChair Preprint no. 10042},

  year = {EasyChair, 2023}}