Download pdf
Research on occlusion face recognition based on deep networks
- 1 Guangdong University of Finance
* Author to whom correspondence should be addressed.
Abstract
Based on the spread of the new crown epidemic, the use of masks has been popularized, so it has a significant impact on the development of face recognition under the cover. The study of how to improve the performance of face recognition under occlusion conditions is also an important topic in the field of face recognition in the future. At the same time, the neural network model is one of the most important models in deep learning, in the field of image classification, face recognition based on deep network has also been proved to be an efficient feature extraction method, this paper divides the face recognition method based on occlusion into two categories: local feature class based on non-occlusion area and feature class based on recognition occlusion area; The basic processes of these two types of methods are summarized, and the specific cases of these two types of occlusion face recognition methods are analyzed. Further summarize the advantages and disadvantages of each and elaborate them. At the end of the article, the shortcomings and future development trends of the current shielding face recognition research are summarized.
Keywords
occlusion face recognition, neural networks, deep learning.
[1]. R. Min, A. Hadid, J.L. Dugelay. Improving the recognition of faces occluded by facial accessories. 2011 Conference on Automatic Face Gesture Recognition and Workshops, 442-447.
[2]. Andrés A M, Padovani S, Tepper M, et al. Face recognition on partially occluded images using compressed sensing, 2014, Pattern Recognition Letters, 36: 235-242.
[3]. M.Sharma, S.Prakash, P.Gupta.An efficient partial occluded face recognition system 2013, Neurocomputing, 116. 231-241.
[4]. L. Song, D. Gong, Z. Li, etc. Occlusion Robust Face Recognition Based on Mask Learning with Pairwise Differential Siamese Network. 2019, International Conference on Computer Vision, 773-782.
[5]. Hou Xiankun, Zhou Wei Research on improving the accuracy of mask masking face recognition 2017, Neurocomputing, 12. 137-149.
[6]. Chunfei Ma, June-Young Jung, Seung-Wook Kim, Sung-Jea Ko. Random projection-based partial feature extraction for robust face recognition. 2015, Neurocomputing, 149:1232-1244.
[7]. Shaikh Anowarul Fattah, Hafiz Imtiaz. A Spectral Domain Local Feature Extraction Algorithm for Face Recognition. 2011, International Journal of Security, 5(2):169-172.
[8]. Wang Xi; Zhang Wei. Anti-occlusion face recognition algorithm based on a deep convolutional neural network. 2021, Computers & Electrical Engineering, 96:107461.
[9]. Abate A F, Nappi M, Riccio D, et al. 2D and 3D face recognition: A survey. 2007, Pattern Recognition Letters, 28(14): 1885-1906.
[10]. Zhou H, Mian A, Wei L, et al. Recent advances on singlemodal and multimodal face recognition: a survey.2014 IEEE Transactions on Human-Machine Systems, 44(6): 701-716.
[11]. Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation, 2009 IEEE Computer Society, 2, 1-11.
[12]. Ou Weihua Research on partially occluded face recognition based on sparse representation and non-negative matrix decomposition, 2011 IEEE Transactions on Systems, 211-223.
[13]. Li X, Dai D, Zhang X,et al. Structured Sparse Error Coding for Face Recognition with Occlusion. 2013, IEEE Transactions on Image Processing, 22(5):1889-1900.
[14]. S. Ge, J. Li, Q. Ye, Z. Luo, Detection Masked Faces in the Wild with LLE- CNNs, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 426-434.
Cite this article
Fu,W. (2023). Research on occlusion face recognition based on deep networks. Applied and Computational Engineering,11,1-7.
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 2023 International Conference on Mechatronics and Smart Systems
© 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).