Abstract
In this work, a secure multibiometric system is proposed. Three different biometric modalities which are ear, face, and thermal face are considered. The face and thermal face data were taken from USTC NVIE Spontaneous Database, whereas the ear data were collected from IIT Delhi Ear Image Database. For each modality, three feature extraction methods are used and four different classifiers (multilayer perceptron, decision tree, support vector machines, and probabilistic neural network) are trained by using two fusion methods which are matching score level and feature level fusion. According to the results, the individual biometrics are better for the identification problem. However, for the validation problem, both fusion methods give better false acceptance rate/false rejection rate values regarding to individual biometrics.
Highlights
In today’s conditions, security is an essential concept for many domains such as online or mobile banking and controlled access to certain buildings or rooms
Biometric systems may be tricked by using forged biometric samples such as photographs of faces or irises and artificial fingerprints
The use of multibiometric systems has come to the forefront [2]
Summary
In today’s conditions, security is an essential concept for many domains such as online or mobile banking and controlled access to certain buildings or rooms. Biometric systems may be tricked by using forged biometric samples such as photographs of faces or irises and artificial fingerprints. Another disadvantage is that, if a biometric system uses only one kind of biometric measurement, it may not be readable or reachable due to physical conditions. If a biometric system uses only one kind of biometric measurement, it may not be readable or reachable due to physical conditions For these reasons, the use of multibiometric systems has come to the forefront [2]. Systems using biometric features are more secure than encryption systems using passwords, keys, or keycards [3, 4].
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