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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | A Review of Deep Learning-Based Face Recognition Systems: Recent Advances, Challenges, and Future Directions |
| Authors Name: | Anju Kaur , Puneet Devi , Rachna Rajput |
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IJRTI_213714
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| Published Paper Id: | IJRTI2607005 |
| Published In: | Volume 11 Issue 7, July-2026 |
| DOI: | |
| Abstract: | Face recognition is one of the fastest evolving research areas in computer vision due to its wide applications in security, surveillance, healthcare, banking, attendance management and smart authentication systems. Earlier face recognition systems relied on handcrafted feature extraction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP). While these approaches gave reasonable results under controlled environments, their performance degraded significantly when facial images contained variations in illumination, pose, expression, aging or partial occlusion. Deep learning has brought about a revolution in face recognition by allowing for the automatic extraction of highly discriminative facial features with the use of Convolutional Neural Networks (CNNs). State-of-the-art architectures such as DeepFace, VGG-Face, FaceNet, ArcFace, SphereFace2 and MobileFaceNet have led to remarkable improvements in recognition accuracy and computational efficiency. Each of these models involves unique learning strategies such as deep feature representation, embedding learning, angular margin optimization, binary classification and lightweight network design to overcome the limitations of previous techniques. This review paper provides a comprehensive study of the prominent deep learning-based face recognition models. The reviewed methods are analyzed based on their architectural design, learning objectives, recognition performance, advantages and limitations. Furthermore, a comparative analysis is provided to highlight the evolution of face recognition technologies. The paper also discusses current research challenges, including pose variation, illumination changes, occlusion, demographic bias, privacy preservation, and computational complexity. Finally, possible future research directions such as Vision Transformers, self-supervised learning, explainable artificial intelligence and lightweight edge-based face recognition systems are discussed. The review shows that while deep learning has greatly improved face recognition performance, further research is needed to design systems that are accurate, efficient, secure and ethically responsible for real-world deployment.. |
| Keywords: | Face Recognition, Deep Learning, Convolutional Neural Networks, DeepFace, VGG-Face, FaceNet, ArcFace, SphereFace2, MobileFaceNet, Biometrics. |
| Cite Article: | "A Review of Deep Learning-Based Face Recognition Systems: Recent Advances, Challenges, and Future Directions", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 7, page no.a47-a54, July-2026, Available :http://www.ijrti.org/papers/IJRTI2607005.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2607005
Registration ID:213714
Published In: Volume 11 Issue 7, July-2026
DOI (Digital Object Identifier):
Page No: a47-a54 Country: Bathinda, Punjab, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2607005 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2607005 |
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ISSN: 2456-3315
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