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For example, we and others in this emerging field are relying more and more on the use of multiple biometric modalities, such as a combination of fingerprint and voice biometrics.
Recently, some researchers have investigated the possibility of using multiple biometric modalities to continuously authenticate the user [9, 10].
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Especially, since the future of smartphone security is 'multi-modal' aka combining multiple biometric measurements (face, iris, fingerprint) simultaneously.
The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities.
One solution to these problems is to use multiple biometric traits for recognition, and systems of this type are known as multi-modal biometric systems [5].
Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris.
Compared to uni-biometric systems, multi-biometric systems, which fuse multiple biometric features, can improve recognition accuracy and security.
Researches have demonstrated that the overall performance of these recognition systems significantly improves upon the inclusion of "soft" biometric traits in addition to the conventional primary biometric modalities.
A wide variety of biometric modalities have been tested so far but several factors paralyze the accuracy of mono-modal biometric systems.
In this paper, we present a multibiometric system based on two different biometric modalities, palmprint and fingerprint.
Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications.
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