Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50
In today's rapidly evolving world, where technology is progressing swiftly, there is an increasing demand for facial recognition systems. Technologies are similar to digital forensics in that they can recognize people by scanning faces. However, one key problem they confront is dealing with co...
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| Format: | Article |
| Language: | English |
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Sukkur IBA University
2025-01-01
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| Series: | Sukkur IBA Journal of Emerging Technologies |
| Subjects: | |
| Online Access: | https://journal.iba-suk.edu.pk:8089/SIBAJournals/index.php/sjet/article/view/1499 |
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| author | Eiman Wahab Wajeeha Shafique Habiba Amir Sameena Javed Muhammad Marouf |
| author_facet | Eiman Wahab Wajeeha Shafique Habiba Amir Sameena Javed Muhammad Marouf |
| author_sort | Eiman Wahab |
| collection | DOAJ |
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In today's rapidly evolving world, where technology is progressing swiftly, there is an increasing demand for facial recognition systems. Technologies are similar to digital forensics in that they can recognize people by scanning faces. However, one key problem they confront is dealing with covered or occluded faces, which might restrict recognition of faces in real-world situations. To overcome this issue, we created a system that is capable of identifying individuals even when their faces are veiled. We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. Further we have conducted feature extraction and pre-processing on our self-created dataset. Our project utilizes the power of deep learning model: Residual Network (ResNet50), the form of deep neural network architectures well-suited for the job of features extraction. These features are matched by using Cosine similarity with accuracy of 92%. By leveraging the capabilities in the deep learning algorithms, this project provides a robust solution for automating the recognition of partially occluded faces.
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| format | Article |
| id | doaj-art-7854e6b4eff74e29ab874ea79a0cd47b |
| institution | Kabale University |
| issn | 2616-7069 2617-3115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Sukkur IBA University |
| record_format | Article |
| series | Sukkur IBA Journal of Emerging Technologies |
| spelling | doaj-art-7854e6b4eff74e29ab874ea79a0cd47b2025-01-29T18:30:04ZengSukkur IBA UniversitySukkur IBA Journal of Emerging Technologies2616-70692617-31152025-01-017210.30537/sjet.v7i2.1499Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 Eiman Wahab0Wajeeha Shafique1Habiba Amir2Sameena Javed3Muhammad Marouf4Bahria University Karachi CampusBahria University Karachi CampusBahria University Karachi CampusBahria University Karachi CampusBahria University Karachi Campus In today's rapidly evolving world, where technology is progressing swiftly, there is an increasing demand for facial recognition systems. Technologies are similar to digital forensics in that they can recognize people by scanning faces. However, one key problem they confront is dealing with covered or occluded faces, which might restrict recognition of faces in real-world situations. To overcome this issue, we created a system that is capable of identifying individuals even when their faces are veiled. We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. Further we have conducted feature extraction and pre-processing on our self-created dataset. Our project utilizes the power of deep learning model: Residual Network (ResNet50), the form of deep neural network architectures well-suited for the job of features extraction. These features are matched by using Cosine similarity with accuracy of 92%. By leveraging the capabilities in the deep learning algorithms, this project provides a robust solution for automating the recognition of partially occluded faces. https://journal.iba-suk.edu.pk:8089/SIBAJournals/index.php/sjet/article/view/1499Face recognitionPartially occluded facesMTCNNResNet50Face BiometricFace detection |
| spellingShingle | Eiman Wahab Wajeeha Shafique Habiba Amir Sameena Javed Muhammad Marouf Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 Sukkur IBA Journal of Emerging Technologies Face recognition Partially occluded faces MTCNN ResNet50 Face Biometric Face detection |
| title | Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 |
| title_full | Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 |
| title_fullStr | Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 |
| title_full_unstemmed | Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 |
| title_short | Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 |
| title_sort | robust face detection and identification under occlusion using mtcnn and resnet50 |
| topic | Face recognition Partially occluded faces MTCNN ResNet50 Face Biometric Face detection |
| url | https://journal.iba-suk.edu.pk:8089/SIBAJournals/index.php/sjet/article/view/1499 |
| work_keys_str_mv | AT eimanwahab robustfacedetectionandidentificationunderocclusionusingmtcnnandresnet50 AT wajeehashafique robustfacedetectionandidentificationunderocclusionusingmtcnnandresnet50 AT habibaamir robustfacedetectionandidentificationunderocclusionusingmtcnnandresnet50 AT sameenajaved robustfacedetectionandidentificationunderocclusionusingmtcnnandresnet50 AT muhammadmarouf robustfacedetectionandidentificationunderocclusionusingmtcnnandresnet50 |