Robust face recognition based on multi-task convolutional neural network

Huilin Ge; Yuewei Dai; Zhiyu Zhu; Biao Wang; Huilin Ge; Yuewei Dai; Zhiyu Zhu; Biao Wang

doi:10.3934/mbe.2021329

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6638-6651. doi: 10.3934/mbe.2021329

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Research article Special Issues

Robust face recognition based on multi-task convolutional neural network

School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Academic Editor: Weining Wu

Received: 07 May 2021 Accepted: 26 July 2021 Published: 05 August 2021

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
Citation: Huilin Ge, Yuewei Dai, Zhiyu Zhu, Biao Wang. Robust face recognition based on multi-task convolutional neural network[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6638-6651. doi: 10.3934/mbe.2021329

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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.

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