Enhancement Ear-based Biometric System Using a Modified AdaBoost Method
The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accur...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Arabic |
| Published: |
College of Science for Women, University of Baghdad
2022-12-01
|
| Series: | Baghdad Science Journal |
| Subjects: | |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6322 |
| _version_ | 1811316503974248448 |
|---|---|
| author | Abdulkareem Merhej Radhi Subhi Aswad Mohammed |
| author_facet | Abdulkareem Merhej Radhi Subhi Aswad Mohammed |
| author_sort | Abdulkareem Merhej Radhi |
| collection | DOAJ |
| description |
The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed system's performance. method, the classification accuracy has been compared using different types of classifiers. These classifiers are Naïve Bayesian, KNN, J48, and SVM. The range of the identification accuracy for all the processed databases using the proposed scenario is between (%93.8- %97.8). The system was executed using MATHLAB R2017, 2.10 GHz processor, and 4 GB RAM.
|
| first_indexed | 2024-04-13T11:51:05Z |
| format | Article |
| id | doaj.art-e786c166221e488698c682ca1cf77102 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-04-13T11:51:05Z |
| publishDate | 2022-12-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-e786c166221e488698c682ca1cf771022022-12-22T02:48:03ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862022-12-0119610.21123/bsj.2022.6322Enhancement Ear-based Biometric System Using a Modified AdaBoost MethodAbdulkareem Merhej Radhi0Subhi Aswad Mohammed1Department of Computer Science, College of Sciences, Al-Nahrain University, Baghdad, IraqDepartment of Computer Engineering, Al-Farabi University, Baghdad, Iraq The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed system's performance. method, the classification accuracy has been compared using different types of classifiers. These classifiers are Naïve Bayesian, KNN, J48, and SVM. The range of the identification accuracy for all the processed databases using the proposed scenario is between (%93.8- %97.8). The system was executed using MATHLAB R2017, 2.10 GHz processor, and 4 GB RAM. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6322AdaBoost, Classifier, Ear, KNN, RMSE, SIFT, SVM |
| spellingShingle | Abdulkareem Merhej Radhi Subhi Aswad Mohammed Enhancement Ear-based Biometric System Using a Modified AdaBoost Method Baghdad Science Journal AdaBoost, Classifier, Ear, KNN, RMSE, SIFT, SVM |
| title | Enhancement Ear-based Biometric System Using a Modified AdaBoost Method |
| title_full | Enhancement Ear-based Biometric System Using a Modified AdaBoost Method |
| title_fullStr | Enhancement Ear-based Biometric System Using a Modified AdaBoost Method |
| title_full_unstemmed | Enhancement Ear-based Biometric System Using a Modified AdaBoost Method |
| title_short | Enhancement Ear-based Biometric System Using a Modified AdaBoost Method |
| title_sort | enhancement ear based biometric system using a modified adaboost method |
| topic | AdaBoost, Classifier, Ear, KNN, RMSE, SIFT, SVM |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6322 |
| work_keys_str_mv | AT abdulkareemmerhejradhi enhancementearbasedbiometricsystemusingamodifiedadaboostmethod AT subhiaswadmohammed enhancementearbasedbiometricsystemusingamodifiedadaboostmethod |