The Development of a Modified Ear Recognition System for Personnel Identification

Background: This study proposed a Match Region Localization (MRL) Ear Recognition System (ERS). Captured ear images were pre-processed through cropping and enhancement. The preprocessed ear images were segmented using the proposed MRL segmentation algorithm and divided into 160 sub-images. The principal features of the segmented ear images were extracted and used in template generation. k-nearest neighbor classifiers with Euclidean distance metrics were applied in the classification. Objective: The proposed ERS exhibited a recognition accuracy of 97.7%. Other publicly available ear datasets can be tested using the proposed system for cross-database comparison and can be improved by reducing their errors. Methods: This research follows four major stages, namely, the development of a PCA-based ear recognition algorithm, implementation of the developed algorithm, determination of the optimum ear segmentation method, and evaluation of the performance of the technique. Results: The False Acceptance Rate (FAR) of the developed Ear Recognition System (ERS) is 0.06. This result implies that six out of every 100 intruders will be falsely accepted. Conclusion: The developed ERS outperforms the existing ERS by approximately 24.61% in terms of system recognition accuracy; the developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms.