Face Recognition: A Literature Review

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.

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