A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade
Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques f...
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MDPI AG
2023-03-01
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| Online Access: | https://www.mdpi.com/2078-2489/14/3/192 |
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| author | Oyediran George Oyebiyi Adebayo Abayomi-Alli Oluwasefunmi ‘Tale Arogundade Atika Qazi Agbotiname Lucky Imoize Joseph Bamidele Awotunde |
| author_facet | Oyediran George Oyebiyi Adebayo Abayomi-Alli Oluwasefunmi ‘Tale Arogundade Atika Qazi Agbotiname Lucky Imoize Joseph Bamidele Awotunde |
| author_sort | Oyediran George Oyebiyi |
| collection | DOAJ |
| description | Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms. |
| first_indexed | 2024-03-11T06:23:47Z |
| format | Article |
| id | doaj.art-2297a6cf10124c71998a2d8952603861 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T06:23:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-2297a6cf10124c71998a2d89526038612023-11-17T11:44:22ZengMDPI AGInformation2078-24892023-03-0114319210.3390/info14030192A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last DecadeOyediran George Oyebiyi0Adebayo Abayomi-Alli1Oluwasefunmi ‘Tale Arogundade2Atika Qazi3Agbotiname Lucky Imoize4Joseph Bamidele Awotunde5Department of Computer Science, Federal University of Agriculture, Abeokuta 110124, NigeriaDepartment of Computer Science, Federal University of Agriculture, Abeokuta 110124, NigeriaDepartment of Computer Science, Federal University of Agriculture, Abeokuta 110124, NigeriaCentre for Lifelong Learning, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, BruneiDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, NigeriaDepartment of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin 240003, NigeriaBiometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.https://www.mdpi.com/2078-2489/14/3/192biometric technologyear recognition systemsfeature extractionclassification methodsconvolutional neural networkrestricted Boltzmann machine |
| spellingShingle | Oyediran George Oyebiyi Adebayo Abayomi-Alli Oluwasefunmi ‘Tale Arogundade Atika Qazi Agbotiname Lucky Imoize Joseph Bamidele Awotunde A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade Information biometric technology ear recognition systems feature extraction classification methods convolutional neural network restricted Boltzmann machine |
| title | A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade |
| title_full | A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade |
| title_fullStr | A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade |
| title_full_unstemmed | A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade |
| title_short | A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade |
| title_sort | systematic literature review on human ear biometrics approaches algorithms and trend in the last decade |
| topic | biometric technology ear recognition systems feature extraction classification methods convolutional neural network restricted Boltzmann machine |
| url | https://www.mdpi.com/2078-2489/14/3/192 |
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