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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/amv24lmzarcebbfkxvuo6hhnpq" style="color: black;">IET Biometrics</a>
With the widespread of computing and mobile devices, authentication using biometrics has received greater attention. Although biometric systems usually provide efficient solutions, the recognition performance tends to be affected over time due to changing conditions and the ageing of biometric data, which results in intra-class variability. This issue is one of the leading causes of the high false rejection rate in biometric authentication systems. Fortunately, this issue has been addressed by<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/bme2.12060">doi:10.1049/bme2.12060</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rjaupc3cpzcg5nkipaqun65gjy">fatcat:rjaupc3cpzcg5nkipaqun65gjy</a> </span>
more »... sing adaptive biometric solutions in which the system gradually adapts to new changes in user biometrics. However, their adaptability to changes may be exploited by an attacker to compromise the stored templates, either to impersonate a specific client or to deny access to him/her. In this work, the authors will carry out a systematic literature review by conducting a comparative study on state-of-the-art solutions for spoofing detection on adaptive authentication systems. This paper will identify the main issues that need to be addressed in adaptive authentication systems. Thus, the authors aim to encourage researchers to develop more robust adaptive solutions to overcome the identified gaps in this research.
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