Abstract
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.
Keywords: Ear recognition, MRL algorithm, feature extraction, k-Nearest neighbor classifiers, personnel identification system, intelligent system.
Graphical Abstract
Related Journals
International Journal of Sensors, Wireless Communications and Control
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
2