Robust face recognition based on multi-task convolutional neural network

Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: I...
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Autor*in:	Huilin Ge [verfasserIn] Yuewei Dai [verfasserIn] Zhiyu Zhu [verfasserIn] Biao Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement

Übergeordnetes Werk:	In: Mathematical Biosciences and Engineering - AIMS Press, 2020, 18(2021), 5, Seite 6638-6651
Übergeordnetes Werk:	volume:18 ; year:2021 ; number:5 ; pages:6638-6651

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3934/mbe.2021329

Katalog-ID:	DOAJ067587178

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520			\|a Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.
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spelling	10.3934/mbe.2021329 doi (DE-627)DOAJ067587178 (DE-599)DOAJfc147976f2f441c396fb355a04cd28d7 DE-627 ger DE-627 rakwb eng TP248.13-248.65 QA1-939 Huilin Ge verfasserin aut Robust face recognition based on multi-task convolutional neural network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency. multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology Mathematics Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Biao Wang verfasserin aut In Mathematical Biosciences and Engineering AIMS Press, 2020 18(2021), 5, Seite 6638-6651 (DE-627)522894844 (DE-600)2265126-3 15510018 nnns volume:18 year:2021 number:5 pages:6638-6651 https://doi.org/10.3934/mbe.2021329 kostenfrei https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7 kostenfrei https://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTML kostenfrei https://doaj.org/toc/1551-0018 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2021 5 6638-6651
allfields_unstemmed	10.3934/mbe.2021329 doi (DE-627)DOAJ067587178 (DE-599)DOAJfc147976f2f441c396fb355a04cd28d7 DE-627 ger DE-627 rakwb eng TP248.13-248.65 QA1-939 Huilin Ge verfasserin aut Robust face recognition based on multi-task convolutional neural network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency. multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology Mathematics Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Biao Wang verfasserin aut In Mathematical Biosciences and Engineering AIMS Press, 2020 18(2021), 5, Seite 6638-6651 (DE-627)522894844 (DE-600)2265126-3 15510018 nnns volume:18 year:2021 number:5 pages:6638-6651 https://doi.org/10.3934/mbe.2021329 kostenfrei https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7 kostenfrei https://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTML kostenfrei https://doaj.org/toc/1551-0018 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2021 5 6638-6651
allfieldsGer	10.3934/mbe.2021329 doi (DE-627)DOAJ067587178 (DE-599)DOAJfc147976f2f441c396fb355a04cd28d7 DE-627 ger DE-627 rakwb eng TP248.13-248.65 QA1-939 Huilin Ge verfasserin aut Robust face recognition based on multi-task convolutional neural network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency. multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology Mathematics Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Biao Wang verfasserin aut In Mathematical Biosciences and Engineering AIMS Press, 2020 18(2021), 5, Seite 6638-6651 (DE-627)522894844 (DE-600)2265126-3 15510018 nnns volume:18 year:2021 number:5 pages:6638-6651 https://doi.org/10.3934/mbe.2021329 kostenfrei https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7 kostenfrei https://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTML kostenfrei https://doaj.org/toc/1551-0018 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2021 5 6638-6651
allfieldsSound	10.3934/mbe.2021329 doi (DE-627)DOAJ067587178 (DE-599)DOAJfc147976f2f441c396fb355a04cd28d7 DE-627 ger DE-627 rakwb eng TP248.13-248.65 QA1-939 Huilin Ge verfasserin aut Robust face recognition based on multi-task convolutional neural network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency. multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology Mathematics Yuewei Dai verfasserin aut Zhiyu Zhu verfasserin aut Biao Wang verfasserin aut In Mathematical Biosciences and Engineering AIMS Press, 2020 18(2021), 5, Seite 6638-6651 (DE-627)522894844 (DE-600)2265126-3 15510018 nnns volume:18 year:2021 number:5 pages:6638-6651 https://doi.org/10.3934/mbe.2021329 kostenfrei https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7 kostenfrei https://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTML kostenfrei https://doaj.org/toc/1551-0018 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2021 5 6638-6651
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callnumber-first	T - Technology
author	Huilin Ge
spellingShingle	Huilin Ge misc TP248.13-248.65 misc QA1-939 misc multi-task cnn misc image recognition misc peak signal-to-noise ratio misc structural similarity index measurement misc Biotechnology misc Mathematics Robust face recognition based on multi-task convolutional neural network
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topic_title	TP248.13-248.65 QA1-939 Robust face recognition based on multi-task convolutional neural network multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement
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topic_unstemmed	misc TP248.13-248.65 misc QA1-939 misc multi-task cnn misc image recognition misc peak signal-to-noise ratio misc structural similarity index measurement misc Biotechnology misc Mathematics
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title	Robust face recognition based on multi-task convolutional neural network
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title_full	Robust face recognition based on multi-task convolutional neural network
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title_sort	robust face recognition based on multi-task convolutional neural network
callnumber	TP248.13-248.65
title_auth	Robust face recognition based on multi-task convolutional neural network
abstract	Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.
abstractGer	Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.
abstract_unstemmed	Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN). Methods: In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN. Results: The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect. Conclusions: This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.
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title_short	Robust face recognition based on multi-task convolutional neural network
url	https://doi.org/10.3934/mbe.2021329 https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7 https://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTML https://doaj.org/toc/1551-0018
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author2	Yuewei Dai Zhiyu Zhu Biao Wang
author2Str	Yuewei Dai Zhiyu Zhu Biao Wang
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doi_str	10.3934/mbe.2021329
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up_date	2024-07-04T01:30:43.496Z
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Robust face recognition based on multi-task convolutional neural network

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