Robust face recognition based on multi-task convolutional neural network
Robust face recognition based on multi-task convolutional neural network
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DOI：
10.3934/mbe.2021329
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发表时间：
2021-01-01
影响因子：
2.6
通讯作者：
Wang, Biao
中科院分区：
文献类型：
--
作者：
Ge, Huilin;Dai, Yuewei;Wang, Biao
目的：由于人脸图像缺乏先验知识，光照变化大，背景复杂，人脸识别的准确率较低。针对这一问题，我们提出了一种基于多任务卷积神经网络的人脸检测与识别算法。方法：在本文中，MTCNN主要采用三层级联网络，并采用候选盒+分类器的思想进行快速高效的人脸识别。该模型在我们收集的50个人脸的数据库上进行训练，并使用峰值信噪比(PSNR)、结构相似性指数(SSIM)和接收器操作特征(ROC)曲线对MTCNN、Region-CNN(R-CNN)和更快的R-CNN进行了分析。结果：该技术的平均PSNR比R-CNN高1.24分贝，比快速R-CNN高0.94分贝。MTCNN的平均SSIM值比R-CNN高10.3%，比更快的R-CNN高8.7%。MTCNN的曲线下面积(AUC)为97.56%，R-CNN的AUC为91.24%，快速R-CNN的AUC为92.01%。MTCNN在人脸识别方面的综合表现最好。对于具有缺陷特征的人脸图像，MTCNN仍然具有最好的效果。结论：该算法能在一定程度上有效提高人脸识别能力。人脸检测的准确率和误检率的降低，不仅可以更好地应用于重点场所，保障人民群众的财产安全和安全，提高安全性，还可以更好地减少人力资源浪费，提高效率。
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.