Personal Recognition Using Fusion of Ear and Iris Biometrics

From security point of view, identity of a person is of critical importance. Increased threats to conventional security methods of personal identification has given rise to personal identification and verification systems based on biometrics. These systems establish human identity using individual's physical or behavioral attributes. These attributes besides being universal, unique and permanent need to be acceptable as well. One such biometric modality that has potential for further exploration is Ear. As a step in this direction, we have made an attempt to explore the potential of time series based Autoregressive (AR) modelling. Performance of AR model using ear biometrics motivated us to make an attempt for 100% RR of a person based on multimodal biometrics. After a study of state of art biometric modalities and potential of second order texture based methods having capability to extract information beyond visual perception, we found it reasonable to fuse AR based feature vector of Ear shape with second order texture based statistics of biometric modality Iris.