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...

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Autores principales:	Huilin Ge, Yuewei Dai, Zhiyu Zhu, Biao Wang
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7
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spelling	oai:doaj.org-article:fc147976f2f441c396fb355a04cd28d72021-11-11T02:01:16ZRobust face recognition based on multi-task convolutional neural network10.3934/mbe.20213291551-0018https://doaj.org/article/fc147976f2f441c396fb355a04cd28d72021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021329?viewType=HTMLhttps://doaj.org/toc/1551-0018Purpose: 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.Huilin Ge Yuewei DaiZhiyu ZhuBiao WangAIMS Pressarticlemulti-task cnnimage recognitionpeak signal-to-noise ratiostructural similarity index measurementBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6638-6651 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	multi-task cnn image recognition peak signal-to-noise ratio structural similarity index measurement Biotechnology TP248.13-248.65 Mathematics QA1-939 Huilin Ge Yuewei Dai Zhiyu Zhu Biao Wang Robust face recognition based on multi-task convolutional neural network
description	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.
format	article
author	Huilin Ge Yuewei Dai Zhiyu Zhu Biao Wang
author_facet	Huilin Ge Yuewei Dai Zhiyu Zhu Biao Wang
author_sort	Huilin Ge
title	Robust face recognition based on multi-task convolutional neural network
title_short	Robust face recognition based on multi-task convolutional neural network
title_full	Robust face recognition based on multi-task convolutional neural network
title_fullStr	Robust face recognition based on multi-task convolutional neural network
title_full_unstemmed	Robust face recognition based on multi-task convolutional neural network
title_sort	robust face recognition based on multi-task convolutional neural network
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/fc147976f2f441c396fb355a04cd28d7
work_keys_str_mv	AT huilinge robustfacerecognitionbasedonmultitaskconvolutionalneuralnetwork AT yueweidai robustfacerecognitionbasedonmultitaskconvolutionalneuralnetwork AT zhiyuzhu robustfacerecognitionbasedonmultitaskconvolutionalneuralnetwork AT biaowang robustfacerecognitionbasedonmultitaskconvolutionalneuralnetwork
_version_	1718439646435737600

Robust face recognition based on multi-task convolutional neural network

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