Analysis of face recognition technology based on deep learning

Xinya Wang

doi:10.54254/2755-2721/22/20231226

Research Article

Open access

Published on 23 October 2023

Download pdf

Wang,X. (2023). Analysis of face recognition technology based on deep learning. Applied and Computational Engineering,22,258-264.

Export citation

Analysis of face recognition technology based on deep learning

Xinya Wang *^,1,

¹ Dalian University of Foreign Languages

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/22/20231226

Abstract

The emergence of face recognition technology has brought great convenience to human society. After years of research and improvement, face recognition technology has matured. This paper mainly studies face recognition technology based on deep learning, and introduces five main applications, including occlusion face recognition, 3D face recognition, and expression recognition. The typical algorithms listed show that, compared with the traditional feature face algorithms and local binary patterns, algorithms such as DeepFace, FaceNet, ResNet, etc. have made significant progress in recognition efficiency and accuracy in recent years. After combining the development status of face recognition technology with the problems and security risks of the current technology, this paper proposes three solutions: increasing the amount of data, optimizing the model, and government control. In the future, legal data sharing and code open source will be major progress in this field, and dynamic face recognition technology will also be widely used.

Keywords

deep learning, face recognition algorithm, problems and solutions, prospects

View pdf

References

[1]. Nielsen, Michael A. Neural Networks and Deep Learning. Determination Press, 2015.

[2]. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. Proceedings of the British Machine Vision Conference, 2015(28), 1-12.

[3]. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823.

[4]. Schroff, F., Kalenichenko, D. and Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815-823).

[5]. Hu, W., Zhang, X., Luo, L., He, L., & Liang, S. (2021). A deep cascaded convolutional network for multi-class face recognition with occlusion. Neural Computing and Applications, 33(16), 8699-8712.

[6]. E. R.ascimento, S. Rigo, T. O. dos Santos, R. de Oliveira, and X. Mei,archical 3D Face Recognition using Deep Learning on Depth and Normal Data," in 2022 IEEE International Conference on Image Processing (ICIP), Sep. 25-28, 2022, Melbourne, Australia, pp. 4053-4057.

[7]. Wang, K., Yan, R., Li, J., & Shi, Y. (2022). Residual Dense Network for Facial Expression Recognition. IEEE Access, 10, 13398-13408.

[8]. Sun, J., Zhang, C., He, Y., & Liu, Q. (2019). Head pose estimation with cascaded multi-view convolutional. Pattern Recognition, 86, 26-38.

[9]. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1701-1708).

[10]. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 815-823.

[11]. He K M，Zhang X Y，Ren S Q，Sun J. Deep Residual Learning for Image Recognition[C].Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas，NV，USA：IEEE，2016：770-778.

[12]. Li, S. Z. (2011). Handbook of face recognition. New York, NY: Springer Science+Business Media, LLC.

[13]. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. Proceedings of the British Machine Vision Conference (BMVC), 2015, 41.1-41.12.

[14]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

[15]. Nagaraja, G., Kumar, A., Raghavendra, R., & Basavaraju, T. G. (2018). An Experimental Investigation of the Effects of Camera Characteristics on Face Recognition Performance. International Journal of Engineering and Technology, 7(4.31), 537-541.

[16]. Doe, J. "Towards Facial Recognition Privacy: A Systematic Literature Review." Mobile Networks and Applications. 2021, doi:10.1007/s11036-021-01786-6.

[17]. Doe, J. "Data Security and Privacy Protection for Facial Recognition Technology: A Review." Journal of Network and Computer Applications. 2020, doi:10.1016/j.jnca.2020.102579.

[18]. Schroff, F., Kalenichenko, D. and Philbin, J., 2015. FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).

Cite this article

Wang,X. (2023). Analysis of face recognition technology based on deep learning. Applied and Computational Engineering,22,258-264.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.22

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).