Cargando…
A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism
Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and backgrou...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879688/ https://www.ncbi.nlm.nih.gov/pubmed/36711196 http://dx.doi.org/10.1155/2023/2501022 |
_version_ | 1784878746459701248 |
---|---|
author | Qi, Xiaobo Wu, Chenxu Shi, Ying Qi, Hui Duan, Kaige Wang, Xiaobin |
author_facet | Qi, Xiaobo Wu, Chenxu Shi, Ying Qi, Hui Duan, Kaige Wang, Xiaobin |
author_sort | Qi, Xiaobo |
collection | PubMed |
description | Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively. |
format | Online Article Text |
id | pubmed-9879688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-98796882023-01-27 A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism Qi, Xiaobo Wu, Chenxu Shi, Ying Qi, Hui Duan, Kaige Wang, Xiaobin Comput Intell Neurosci Research Article Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively. Hindawi 2023-01-19 /pmc/articles/PMC9879688/ /pubmed/36711196 http://dx.doi.org/10.1155/2023/2501022 Text en Copyright © 2023 Xiaobo Qi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Qi, Xiaobo Wu, Chenxu Shi, Ying Qi, Hui Duan, Kaige Wang, Xiaobin A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title | A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title_full | A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title_fullStr | A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title_full_unstemmed | A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title_short | A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism |
title_sort | convolutional neural network face recognition method based on bilstm and attention mechanism |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879688/ https://www.ncbi.nlm.nih.gov/pubmed/36711196 http://dx.doi.org/10.1155/2023/2501022 |
work_keys_str_mv | AT qixiaobo aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT wuchenxu aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT shiying aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT qihui aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT duankaige aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT wangxiaobin aconvolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT qixiaobo convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT wuchenxu convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT shiying convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT qihui convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT duankaige convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism AT wangxiaobin convolutionalneuralnetworkfacerecognitionmethodbasedonbilstmandattentionmechanism |