| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Sao Paulo, Sao Paulo - Brazil
[2] Univ Fed Sao Paulo, Sao Paulo - Brazil
[3] Univ Sao Paulo, Dept Comp Sci, Sao Paulo - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | IEEE INTELLIGENT SYSTEMS; v. 33, n. 2, p. 19-28, MAR-APR 2018. |
| Web of Science Citations: | 1 |
| Abstract | |
With the increased availability of online services, enhanced authentication mechanisms-including biometric systems-are necessary. However, recent studies show that biometric features can change. Consequently, recognition performance can be affected over time. Adaptive biometric systems that can automatically adapt the biometric reference have been proposed to deal with this problem. Frequently, these systems use query samples classified as genuine to adapt the biometric reference. Despite good results, there are concerns regarding their robustness. This article investigates using an ensemble of classifiers to increase these systems' robustness. Ensembles can improve the recognition performance of decision models, providing a more stable classification decision. The authors explore questions regarding the application of ensembles to adaptive biometric systems using one-class classification algorithms, and offer a proposal to automatically adapt the meta classifier over time. (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 |