Indexing of Ears using Radial basis Function Neural Network for Personal Identification

This paper elaborates a novel method to recognize persons using ear biometrics. We propose a method to index the ears using Radial Basis Function Neural Networks (RBFNN). In order to obtain the invariant features, an ear has been considered as a planar surface of irregular shape. The shape based features like planar area, moment of inertia with respect to minor and major axes, and radii of gyration with respect to minor and major axes are considered. The indexing equation is generated using the weights, centroids and kernel function of the stabilized RBFN network. The so developed indexing equation was tested and validated. The analysis of the equation revealed 95.4% recognition accuracy. The retrieval rate of personal details became faster by an average of 13.8% when the database was organized as per the indices. Further, the three groups elicited by RBFNN were evaluated for parameters like entropy, precision, recall, specificity and F-measure. And all the parameters are found to be excellent in terms of their values and thus showcase the adequacy of the indexing model.

@article{Jayaram2015,
  title     = {Indexing of Ears using Radial basis Function Neural Network for Personal Identification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {7},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {M. A. Jayaram and Prashanth G.K and M.Anusha},
  doi       = {10.14569/IJACSA.2015.060705},
  url       = {https://doi.org/10.14569/IJACSA.2015.060705}
}