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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2578-1863 |