Volume 8, Issue 1, February 2020, Page: 36-41
The Research on Face Recognition and Segmentation Based on Intelligent Background
Jiangtao Wang, College of Network Communication, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, China
Received: Feb. 29, 2020;       Published: Apr. 14, 2020
DOI: 10.11648/j.jeee.20200801.16      View  229      Downloads  160
Abstract
Affected by factors such as attitude, light, expression, etc., it is impossible to accurately identify the identity in a wireless visual sensor network in an uncontrollable environment. In traditional visual identity recognition, it is necessary to convert uncontrollable factors into controllable and stable feature factors for identity recognition in a relatively uncontrollable environment where the node distribution is relatively complicated. The conversion process leads to long recognition time and low efficiency. An adaptive recognition method for identity features in wireless visual sensing networks based on LBP face recognition is proposed. A strong classifier is obtained for cascading, and the underlying features are extracted. The final Harr face cascade classifier is applied to the face Check it out. The PCA dimensionality reduction processing of the facial area feature vector is performed to obtain the low-dimensional feature vector, the dimensionality reduction coefficient, and the average face of the person. For the face image in the wireless local area, its LBP operation is given. Perform histogram statistics on face feature information, obtain face LBP histograms, and perform feature matching on the face feature database to complete recognition. The improved algorithm has improved the cumulative matching score of traditional algorithms by 17.8%; the accuracy rate has improved It is 32.7%, and the recognition time is shortened by 3.9s. Simulation results show that the proposed algorithm has high accuracy and recognition efficiency.
Keywords
Wireless Sensor, Dimensionality Reduction, Feature Matching, Adaptive Recognition
To cite this article
Jiangtao Wang, The Research on Face Recognition and Segmentation Based on Intelligent Background, Journal of Electrical and Electronic Engineering. Vol. 8, No. 1, 2020, pp. 36-41. doi: 10.11648/j.jeee.20200801.16
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Liu X, Li S, Kong L, et al. Feature-level Frankenstein: Eliminating Variations for Discriminative Recognition [C]. 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2019.
[2]
Liu X, Kumar B. V. K, Yang C, et al. Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets." Proceedings of the European Conference on Computer Vision (ECCV), 2018, 548-565.
[3]
Rao Y, Lu J, Zhou J. Learning Discriminative Aggregation Network for Video-Based Face Recognition and Person Re-identification [J]. International Journal of Computer Vision, 2018 (2).
[4]
Hadad N, Wolf L, Shahar M. Two-Step Disentanglement for Financial Data [J]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 772–780, 2018.
[5]
Kushwaha V, Singh M, Singh R, et al. Disguised Faces in the Wild [C]. 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
[6]
Zellinger W, Grubinger T, Lughofer E, et al. Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning [C]. arXiv: 1702.08811, 2017.
[7]
Cao J, Katzir O, Jiang P, etal. DiDA: Disentangled Synthesis for Domain Adaptation [J]. arXiv preprint arXiv: 1805.08019, 2018.
[8]
Li Y, Tian X, Gong M, et al. Deep Domain Generalization via Conditional Invariant Adversarial Networks [C]. European Conference on Computer Vision. 2018.
[9]
Cao Q, Shen L, Xie W, et al. VGGFace2: A dataset for recognizing faces across pose and age [J]. In Automatic Face & Gesture Recognition, 2018.
[10]
Xie W, Zisserman A. Multicolumn Networks for Face Recognition [J]. ArXiv: 1807.09192, 2018.
[11]
Xie W, Li S, Zisserman A. Comparator Networks [J]. In Proceedings of the European Conference on Computer Vision (ECCV), 2018.
[12]
Bo Y, Chen S. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image [J]. Neurocomputing, 2013, 120 (10): 365-379.
[13]
Maze B, Adams J, Duncan JA, et al. IARPA Janus benchmark-c: Face dataset and protocol [C]. In 2018 International Conference on Biometrics (ICB) 2018.
[14]
Zeng N, Zhang H, Song B, et al. Facial expression recognition via learning deep sparse autoencoders [J]. Neurocomputing, 2017.
[15]
Liu X, Vijay Kumar B. V. K, Jia P, et al. Hard negative generation for identity-disentangled facial expression recognition [J]. Pattern Recognition, 2019, 88: 1-12.
Browse journals by subject