Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network
The performance of most face recognition systems (FRSs) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classifica...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8550634/ |
| _version_ | 1818619205232099328 |
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| author | Muhtahir O. Oloyede Gerhard P. Hancke Herman C. Myburgh |
| author_facet | Muhtahir O. Oloyede Gerhard P. Hancke Herman C. Myburgh |
| author_sort | Muhtahir O. Oloyede |
| collection | DOAJ |
| description | The performance of most face recognition systems (FRSs) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRSs, thus limiting the wide applicability of most FRSs in real-life scenarios. Therefore, it is envisaged that installing more effective pre-processing techniques, in addition to selecting the right features for classification, will significantly improve the performance of FRSs. Hence, in this paper, we propose an FRS, which comprises an effective image enhancement technique for face image preprocessing, alongside a new set of hybrid features. Our image enhancement technique adopts the use of a metaheuristic optimization algorithm for effective face image enhancement, irrespective of the conditions in the unconstrained environment. This results in adding more features to the face image so that there is an increase in recognition performance as compared with the original image. The new hybrid feature is introduced in our FRS to improve the classification performance of the state-of-the-art convolutional neural network architectures. Experiments on standard face databases have been carried out to confirm the improvement in the performance of the face recognition system that considers all the constraints in the face database. |
| first_indexed | 2024-12-16T17:33:47Z |
| format | Article |
| id | doaj.art-09b3a828950245319e13525b0a775af5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:33:47Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-09b3a828950245319e13525b0a775af52022-12-21T22:22:51ZengIEEEIEEE Access2169-35362018-01-016751817519110.1109/ACCESS.2018.28837488550634Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural NetworkMuhtahir O. Oloyede0https://orcid.org/0000-0001-9147-0179Gerhard P. Hancke1Herman C. Myburgh2Department of Electrical Electronic and Computer Engineering, University of Pretoria, Pretoria, South AfricaDepartment of Electrical Electronic and Computer Engineering, University of Pretoria, Pretoria, South AfricaDepartment of Electrical Electronic and Computer Engineering, University of Pretoria, Pretoria, South AfricaThe performance of most face recognition systems (FRSs) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRSs, thus limiting the wide applicability of most FRSs in real-life scenarios. Therefore, it is envisaged that installing more effective pre-processing techniques, in addition to selecting the right features for classification, will significantly improve the performance of FRSs. Hence, in this paper, we propose an FRS, which comprises an effective image enhancement technique for face image preprocessing, alongside a new set of hybrid features. Our image enhancement technique adopts the use of a metaheuristic optimization algorithm for effective face image enhancement, irrespective of the conditions in the unconstrained environment. This results in adding more features to the face image so that there is an increase in recognition performance as compared with the original image. The new hybrid feature is introduced in our FRS to improve the classification performance of the state-of-the-art convolutional neural network architectures. Experiments on standard face databases have been carried out to confirm the improvement in the performance of the face recognition system that considers all the constraints in the face database.https://ieeexplore.ieee.org/document/8550634/Face recognitionimage enhancementhybrid featuresmetaheuristic algorithmsunconstrained environments |
| spellingShingle | Muhtahir O. Oloyede Gerhard P. Hancke Herman C. Myburgh Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network IEEE Access Face recognition image enhancement hybrid features metaheuristic algorithms unconstrained environments |
| title | Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network |
| title_full | Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network |
| title_fullStr | Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network |
| title_full_unstemmed | Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network |
| title_short | Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network |
| title_sort | improving face recognition systems using a new image enhancement technique hybrid features and the convolutional neural network |
| topic | Face recognition image enhancement hybrid features metaheuristic algorithms unconstrained environments |
| url | https://ieeexplore.ieee.org/document/8550634/ |
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