Abstract—A lot of research is going on biometrics security systems due to the high increase in spoofing attacks. To provide enhanced security using biometric applications, researchers showed more interest in multimodal biometrics. Using Multimodal biometrics applications, the complex model structure can be designed which provides a low risk of a spoofing attack. This paper discussed a hybrid model designed using the multilevel fusion of multimodal biometrics. This model considered two biometrics modalities face and finger vein, and also two levels of fusion feature level and decision level. In this work five classifiers Ensemble discriminant, K-Nearest Neighbor, Linear Discriminant, Ensemble subspace K-Nearest Neighbor (ESKNN), and SVM for majority voting are used. In this work rich information image is created by up sampling the image using bilinear interpolation techniques. The proposed model advances the recognition rate over unimodal biometric systems.
Index Terms—Multimodal, biometrics, feature level fusion, decision level fusion.
Arjun B. C. and H. N. Prakash are with Rajeev Institute of Technology, Hassan, Affiliated to Visvesvaraya Technological University, Karnataka, India (e-mail: bc.arjun@gmail.com, prakash_hn@yahoo.com).
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Cite:Arjun B. C. and H. N. Prakash, "Multimodal Biometric Recognition System Using Face and Finger Vein Biometric Traits with Feature and Decision Level Fusion Techniques," International Journal of Computer Theory and Engineering vol. 13, no. 4, pp. 123-128, 2021.
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