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.
| Published in | Journal of Electrical and Electronic Engineering (Volume 8, Issue 1) |
| DOI | 10.11648/j.jeee.20200801.16 |
| Page(s) | 36-41 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Wireless Sensor, Dimensionality Reduction, Feature Matching, Adaptive Recognition
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APA Style
Jiangtao Wang. (2020). The Research on Face Recognition and Segmentation Based on Intelligent Background. Journal of Electrical and Electronic Engineering, 8(1), 36-41. https://doi.org/10.11648/j.jeee.20200801.16
ACS Style
Jiangtao Wang. The Research on Face Recognition and Segmentation Based on Intelligent Background. J. Electr. Electron. Eng. 2020, 8(1), 36-41. doi: 10.11648/j.jeee.20200801.16
AMA Style
Jiangtao Wang. The Research on Face Recognition and Segmentation Based on Intelligent Background. J Electr Electron Eng. 2020;8(1):36-41. doi: 10.11648/j.jeee.20200801.16
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Download@article{10.11648/j.jeee.20200801.16,
author = {Jiangtao Wang},
title = {The Research on Face Recognition and Segmentation Based on Intelligent Background},
journal = {Journal of Electrical and Electronic Engineering},
volume = {8},
number = {1},
pages = {36-41},
doi = {10.11648/j.jeee.20200801.16},
url = {https://doi.org/10.11648/j.jeee.20200801.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20200801.16},
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.},
year = {2020}
}
TY - JOUR T1 - The Research on Face Recognition and Segmentation Based on Intelligent Background AU - Jiangtao Wang Y1 - 2020/04/14 PY - 2020 N1 - https://doi.org/10.11648/j.jeee.20200801.16 DO - 10.11648/j.jeee.20200801.16 T2 - Journal of Electrical and Electronic Engineering JF - Journal of Electrical and Electronic Engineering JO - Journal of Electrical and Electronic Engineering SP - 36 EP - 41 PB - Science Publishing Group SN - 2329-1605 UR - https://doi.org/10.11648/j.jeee.20200801.16 AB - 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. VL - 8 IS - 1 ER -
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