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Also, because the error value calculation is determined based on the mean and variance of each biometric, therefore each user must provide multiple biometric samples to generate the associated means and variances.
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The purpose of sensor-level fusion is to fuse the same biometric samples coming from different sensors or multiple instances captured by the same sensor [13].
A skillful attacker may gather any person's biometric samples and can produce forged biometrics.
Quality assessment of biometric samples is an important challenge for the biometrics research community.
Protecting the privacy of biometric samples has become an active area of research in biometrics.
Compared to uni-biometric systems, multi-biometric systems, which fuse multiple biometric features, can improve recognition accuracy and security.
It is likely, however, that multiple biometric retail systems will make their way into the market.
Multi-biometric cryptosystems, which should be designed to combine multiple biometric characteristics at feature level [21], might bridge the gap between biometric performance and privacy protection [19].
The incorporation of multiple biometric characteristics to biometric cryptosystems has been found to improve biometric performance [7], while the protection of multi-biometric templates is especially crucial as they contain information regarding multiple characteristics of the same subject [21].
Ensembles of multiple sensors and multiple biometric verifications also need various kinds of sensors.
This is expanded through the development of algorithms that consider multiple biometric systems [31].
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