Gabriela Laura Salagean – Doctoral School, University of Petrosani, street Universității, no. 20, 332006, Petroșani, Romania
Monica Leba – Faculty of Mechanical and Electrical Engineering, University of Petrosani, street Universității, no. 20, 332006, Petroșani, Romania
Keywords:
Computer vision;
Face recognition;
Face analysis;
Deep learning
DOI: https://doi.org/10.31410/ITEMA.2023.55
Abstract: Face recognition is a crucial and rapidly evolving field within computer vision and artificial intelligence. The human face is one important element for recognizing a person in a group. The mechanism used by the human brain for face recognition is not yet fully understood, making it a complex problem. Over the past decade, there have been significant advancements in both the accuracy and applicability of face recognition systems. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in achieving state-of-the-art performance in face recognition tasks. This paper proposes a review of research on face recognition techniques, algorithms and existing applications, with their advantages and disadvantages. It also includes a comparison of the results obtained with various algorithms and the related limitations. In the last part, it presents future directions of development in the field of face recognition.
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7th International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2023 – Conference Proceedings, Hybrid (Faculty of Organization and Informatics Varaždin, University of Zagreb, Croatia), October 26, 2023
ITEMA Conference Proceedings published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia
ITEMA conference partners: Faculty of Economics and Business, University of Maribor, Slovenia; Faculty of Organization and Informatics, University of Zagreb, Varaždin; Faculty of Geography, University of Belgrade, Serbia; Institute of Marketing, Poznan University of Economics and Business, Poland; Faculty of Agriculture, Banat’s University of Agricultural Sciences and Veterinary Medicine ”King Michael I of Romania”, Romania
ITEMA Conference 2023 Conference Proceedings: ISBN 978-86-80194-75-2, ISSN 2683-5991, DOI: https://doi.org/10.31410/ITEMA.2023
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.
References
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, present, and future of face recognition: A review. Electronics (Switzerland). MDPI AG. https://doi.org/10.3390/electronics9081188
Anggo, M., & La Arapu. (2018). Face Recognition Using Fisherface Method. Journal of Physics: Conference Series, 1028, 012119. https://doi.org/10.1088/1742-6596/1028/1/012119
Çarıkçı, M., & Özen, F. (2012). A Face Recognition System Based on Eigenfaces Method, Procedia Technology 1,118-123, https://doi.org/10.1016/j.protcy.2012.02.023
Deng, J., Guo, J., & Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. International Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4690–4699.
Deng, J., Zhou, Y., & Zafeiriou, S. (2017). Marginal Loss for Deep Face Recognition. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 2006–2014.
Himanshu, D., & Prabhakar, J. (2023). Face recognition algorithms: a comparative study, International Research Journal of Modernization in Engineering Technology and Science, 05, 9420. www.irjmets.com
Jin, C., & Sheeja, A. (2019). A summary of literature review: face recognition, International Journal of Engineering Sciences & Research Technology, 8(4), 91-94 DOI: 10.5281/zenodo.2631167
Jin, K., Xie, X., Wang, F., Han, X., & Shi, G. (2019). Human Identification Recognition in Surveillance Videos. International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 162-167. DOI: 10.1109/ICMEW.2019.00-93.
Jing, Y., Lu, X., & Gao, S. (2023). 3D face recognition: A comprehensive survey in 2022. Comp. Visual Media 9, 657–685. https://doi.org/10.1007/s41095-022-0317-1
Karthick, S., Selvakumarasamy, S., Arun, C., & Agrawal, P. (2021). Automatic attendance monitoring system using facial recognition through feature-based methods (PCA, LDA)., ScienceDirect. DOI: https://doi.org/10.1016/j.matpr.2021.01.517
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering (EBSE 2007-001). Keele University and Durham University Joint Report.
Ling, H., Wu, J., Huang, J., & Li, P. (2020). Attention-based convolutional neural network for deep face recognition. Multimedia. Tools Appl., 79, 5595–5616. https://doi.org/10.1007/s11042-019-08422-2
Maharani, D. A., Machbub, C., Rusmin, P. H., & Yulianti, L. (2020). Improving the Capability of Real-Time Face Masked Recognition using Cosine Distance. 6th International Conference on Interactive Digital Media (ICIDM). pp 1-6. DOI: 10.1109/ICIDM51048.2020.9339677
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 815–823.
Shan, C., & Gritti, T. (2008). Learning discriminative LBP-histogram bins for facial expression recognition, Proceedings of the British Machine Vision Conference, https://doi.org/10.5244/c.22.27
Sveleba, S., Katerynchuk, I., Karpa, I., Kunyo, I., Ugryn, S., & Ugryn, V. (2019). The Real-Time Face Recognition. 3rd International Conference on Advanced Information and Communications Technologies (AICT) IEEE, 294-297. DOI: 10.1109/AIACT.2019.8847753
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). Deepface Closing the gap to human-level performance in face verification. IEEE Conference on Computer Vision and Pattern Recognition, Columbus. pp 1701–1708.
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018). CosFace: Large Margin Cosine Loss for Deep Face Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00552