Ear Recognition with Neural Networks Based on Fisher and Surf Algorithms

Abstract

This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this research offers an approach using Speeded Up Robust Features (SURF) and Fisher Linear Discriminant Analysis (LDA) as an input of two neural networks with the purpose to detect and recognize a person by the patterns of its ear. It also includes the development of an application with .net to show experimental results of the applied theory. In the preprocessing task, the system adds sturdiness using Hausdorff distance to increase the performance filtering for the subjects to use in the testing stage of the neural network. To perform this study, we worked with the help of Ávila’s police school (Spain), where we built a database with approximately 300 ears. The investigation results shown that the integration of LDA and SURF in neural networks can improve the ear recognition process and provide robustness in changes of illumination and perception.