Multimodal Multilevel Fusion of Face Ear Iris with Multiple Classifications

Abstract

With the advancement in the computational efficiency, there is also simultaneous increase in many efficient and secure biometric systems that are capable for the use of multiple sources of access authorization. Single biometric systems are inefficient and less secure which give rise to the advancement of multimodal biometric systems. Also, fusion of many biometric modalities is high area of interest, and here, many methods are deployed for the fusion of biometric data. Multimodal biometric system provides many evidences for the same person. In this paper, the design of multimodal biometrics based on face, ear, and iris modalities with multilevel fusion-based approach is preferred. In the presented work with multilevel multimodal fusion, 95.09% accuracy has been obtained which is better than highest unimodal accuracy; in this case, it is iris 94.06%. The obtained results are better than similar multimodal fusion-based model with single classifiers such as RNN with 90.58% accuracy and KNN classifier with 91.22% accuracy. So, in this work multilevel fusion of (i) different unimodal methods with (ii) feature level fusion of multiple traits has been proposed for person identification.