Face Recognition approach via Deep and Machine Learning
Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to i...
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| Format: | Article |
| Language: | English |
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College of Education for Pure Sciences
2023-09-01
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| Series: | Wasit Journal for Pure Sciences |
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| Online Access: | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/194 |
| _version_ | 1797288350702370816 |
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| author | Ola N. Kadhim |
| author_facet | Ola N. Kadhim |
| author_sort | Ola N. Kadhim |
| collection | DOAJ |
| description |
Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM). We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier.
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| first_indexed | 2024-03-07T18:48:05Z |
| format | Article |
| id | doaj.art-26618f22754f4e32a94c50f091c5308f |
| institution | Directory Open Access Journal |
| issn | 2790-5233 2790-5241 |
| language | English |
| last_indexed | 2024-03-07T18:48:05Z |
| publishDate | 2023-09-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | Wasit Journal for Pure Sciences |
| spelling | doaj.art-26618f22754f4e32a94c50f091c5308f2024-03-02T02:02:43ZengCollege of Education for Pure SciencesWasit Journal for Pure Sciences2790-52332790-52412023-09-012310.31185/wjps.194Face Recognition approach via Deep and Machine LearningOla N. Kadhim0Department of Computer Science, Faculty of Computer Science & Mathematics, University of Kufa, Najaf, Iraq Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM). We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier. https://wjps.uowasit.edu.iq/index.php/wjps/article/view/194Wavelet Scattering NetworkFace recognitionBiometricdeep learning |
| spellingShingle | Ola N. Kadhim Face Recognition approach via Deep and Machine Learning Wasit Journal for Pure Sciences Wavelet Scattering Network Face recognition Biometric deep learning |
| title | Face Recognition approach via Deep and Machine Learning |
| title_full | Face Recognition approach via Deep and Machine Learning |
| title_fullStr | Face Recognition approach via Deep and Machine Learning |
| title_full_unstemmed | Face Recognition approach via Deep and Machine Learning |
| title_short | Face Recognition approach via Deep and Machine Learning |
| title_sort | face recognition approach via deep and machine learning |
| topic | Wavelet Scattering Network Face recognition Biometric deep learning |
| url | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/194 |
| work_keys_str_mv | AT olankadhim facerecognitionapproachviadeepandmachinelearning |