School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Math Biosci Eng. 2021 Aug 5;18(5):6638-6651. doi: 10.3934/mbe.2021329.
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).
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.
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.
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.
由于缺乏人脸图像的先验知识、光照变化大以及背景复杂等因素，人脸识别的准确性较低。为了解决这个问题，我们提出了一种基于多任务卷积神经网络（MTCNN）的人脸检测和识别算法。
在我们的论文中，MTCNN 主要使用了三个级联网络，并采用候选框加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 个人脸数据库上进行训练，并使用峰值信噪比（PSNR）、结构相似性指数测量（SSIM）和接收者操作特征（ROC）曲线来分析 MTCNN、区域卷积神经网络（R-CNN）和更快的 R-CNN。
该技术的平均 PSNR 比 R-CNN 高 1.24 分贝，比更快的 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%，更快的 R-CNN 的 AUC 为 92.01%。MTCNN 在人脸识别方面具有最佳的综合性能。对于特征有缺陷的人脸图像，MTCNN 仍然具有最佳的效果。
该算法可以在一定程度上有效提高人脸识别的准确率和降低误检率，不仅可以更好地应用于关键场所，保证财产和人员安全，提高安全性，还可以更好地减少人力资源的浪费，提高效率。
