Score normalization applied to adaptive biometric systems

Author(s)

Pisani, Paulo Henrique ^[1] ; Poh, Norman ^[2] ; de Carvalho, Andre C. P. L. F. ^[1] ; Lorena, Ana Carolina ^[3]

Affiliation

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Av Trabalhador Sao Carlense 400, Sao Carlos, SP - Brazil

^[2] Univ Surrey, Dept Comp, Fac Engn & Phys Sci, Guildford, Surrey - England

^[3] Univ Fed Sao Paulo, Inst Ciencia & Tecnol, Rua Talim 330, Sao Jose Dos Campos - Brazil

• Document type: Journal article
• Source: COMPUTERS & SECURITY; v. 70, p. 565-580, SEP 2017.
• Web of Science Citations: 2

Abstract

Biometric authentication systems have certain limitations. Recent studies have shown that biometric features may change over time, which can entail a decrease in recognition performance of the biometric system. An adaptive biometric system addresses this problem by adapting the biometric reference/template over time, thereby tracking the changes automatically. However, the use of these systems usually requires the adoption of a high threshold value to avoid the inclusion of impostor patterns into the genuine biometric reference. In this study, we hypothesize that score normalization procedures, which have been used to improve the recognition performance of biometric systems through a better refinement of their decision, can also improve the overall performance of adaptive systems. With such a normalization, a better threshold choice could also be made, which would then increase the number of genuine samples used for adaptation. To the best of our knowledge, this is the first investigation towards the use of score normalization to enhance adaptive biometric systems dealing with the change of user features over time. Through a systematic experimental design tested on two behavioral biometric traits, the obtained results indeed support our conjecture. Moreover, the experimental results show that the performance gain brought by adaptation can have a higher overall impact than score normalization alone. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 12/22608-8 - Use of data complexity measures in the support of supervised machine learning
• Grantee: Ana Carolina Lorena
• Support Opportunities: Research Grants - Young Investigators Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 12/25032-0 - Biometrics in a Data Flow Context with Immune Algorithms
• Grantee: Paulo Henrique Pisani
• Support Opportunities: Scholarships in Brazil - Doctorate