改进的基于cnn的人脸检测技术在公共管理中的应用

Zhao Zhao
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摘要

在公共管理中,智能人脸识别检测技术起着非常关键的作用,它可以大大提高公共管理的效率,减少工作人员的工作量。针对传统人脸检测算法检测效率低、易过拟合等缺点,本文提出了一种基于卷积神经网络(CNN)的人脸检测模型,并对CNN的结构进行了优化,提高了所提人脸检测模型的准确率和效率。为了解决光照差异导致的人脸检测误差,提出了一种光补偿策略对数据进行预处理;同时,采用高斯曲率滤波算法对人脸图像进行增强,提高后续检测精度。在此基础上,本研究设计了一种基于改进CNN的人脸检测模型。实验表明,该模型的准确率达到99.86%,具有较高的准确率和效率,表明该方法可以提高公共管理的效率,在门禁和签到系统中具有良好的应用前景。
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Application of improved CNN-based face detection technology in public administration
In public management, intelligent face recognition detection technology plays a very crucial role, which can greatly improve the efficiency of public management and reduce the workload of staff. To address the shortcomings of traditional face detection algorithms such as low detection efficiency and easy overfitting, a face detection model based on convolutional neural network (CNN) was proposed in this study, and the structure of CNN was optimized to enhance the accuracy and efficiency of the proposed face detection model. To solve the face detection errors caused by illumination differences, a light compensation strategy was proposed to pre-process the data; meanwhile, a Gaussian curvature filtering algorithm was used to enhance the face image and improve the subsequent detection accuracy. On this basis, a face detection model based on improved CNN was designed in this study. Experiments showed that the accuracy of the model reached 99.86% with high accuracy and efficiency, indicating that such method can improve the efficiency of public management and has good application prospects in access control and check-in systems.
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