A Scoping Review of Literature on Deep Learning Techniques for Face Recognition
Deep learning has led to the creation of facial recognition technologies using convolutional neural networks (CNNs). This preliminary study explores the application of CNN architectures in face recognition to gain a deeper understanding of the challenges and methodologies in the field. The study sys...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Human Behavior and Emerging Technologies |
| Online Access: | http://dx.doi.org/10.1155/hbe2/5979728 |
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| author | Andisani Nemavhola Serestina Viriri Colin Chibaya |
| author_facet | Andisani Nemavhola Serestina Viriri Colin Chibaya |
| author_sort | Andisani Nemavhola |
| collection | DOAJ |
| description | Deep learning has led to the creation of facial recognition technologies using convolutional neural networks (CNNs). This preliminary study explores the application of CNN architectures in face recognition to gain a deeper understanding of the challenges and methodologies in the field. The study systematically reviewed relevant literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework. Out of 3622 eligible papers, 266 were included in the review, with 47% proposing new techniques and 1% focusing on method implementation and comparison. Most studies used images rather than video as training or testing data, with 78% using clean data and only 7% utilizing occluded and clean data. It was observed that traditional CNN architectures were predominantly employed. The study identified a lack of research on the implementation and definition of CNN architectures, the development of facial recognition models using both clean and occluded images and videos, and the exploration of nontraditional CNN architectures. The challenges affecting facial recognition included occlusion, distance from the camera, camera angle, and lighting conditions. This preliminary assessment provides an insight into the use of CNN in face recognition and suggests that nontraditional CNN architectures could be further explored in future research. |
| format | Article |
| id | doaj-art-c5ab0cebf62c46129afbc284737cf368 |
| institution | Kabale University |
| issn | 2578-1863 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Human Behavior and Emerging Technologies |
| spelling | doaj-art-c5ab0cebf62c46129afbc284737cf3682025-01-17T00:00:01ZengWileyHuman Behavior and Emerging Technologies2578-18632025-01-01202510.1155/hbe2/5979728A Scoping Review of Literature on Deep Learning Techniques for Face RecognitionAndisani Nemavhola0Serestina Viriri1Colin Chibaya2School of Consumer Intelligence and Information SystemsSchool of MathematicsSchool of Natural and Applied SciencesDeep learning has led to the creation of facial recognition technologies using convolutional neural networks (CNNs). This preliminary study explores the application of CNN architectures in face recognition to gain a deeper understanding of the challenges and methodologies in the field. The study systematically reviewed relevant literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework. Out of 3622 eligible papers, 266 were included in the review, with 47% proposing new techniques and 1% focusing on method implementation and comparison. Most studies used images rather than video as training or testing data, with 78% using clean data and only 7% utilizing occluded and clean data. It was observed that traditional CNN architectures were predominantly employed. The study identified a lack of research on the implementation and definition of CNN architectures, the development of facial recognition models using both clean and occluded images and videos, and the exploration of nontraditional CNN architectures. The challenges affecting facial recognition included occlusion, distance from the camera, camera angle, and lighting conditions. This preliminary assessment provides an insight into the use of CNN in face recognition and suggests that nontraditional CNN architectures could be further explored in future research.http://dx.doi.org/10.1155/hbe2/5979728 |
| spellingShingle | Andisani Nemavhola Serestina Viriri Colin Chibaya A Scoping Review of Literature on Deep Learning Techniques for Face Recognition Human Behavior and Emerging Technologies |
| title | A Scoping Review of Literature on Deep Learning Techniques for Face Recognition |
| title_full | A Scoping Review of Literature on Deep Learning Techniques for Face Recognition |
| title_fullStr | A Scoping Review of Literature on Deep Learning Techniques for Face Recognition |
| title_full_unstemmed | A Scoping Review of Literature on Deep Learning Techniques for Face Recognition |
| title_short | A Scoping Review of Literature on Deep Learning Techniques for Face Recognition |
| title_sort | scoping review of literature on deep learning techniques for face recognition |
| url | http://dx.doi.org/10.1155/hbe2/5979728 |
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