Affiliations: [a] School of Public Policy and Management (School of Emergency Management), China University of Mining and Technology, Xuzhou, Jiangsu 221116, China | [b] General Affairs Department, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China | E-mail: zhaozhaocumt@163.com
Correspondence:
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Corresponding author: General Affairs Department, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China. E-mail: zhaozhaocumt@163.com.
Abstract: 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.
Keywords: CNN, face detection, public administration, gaussian curvature filtering, image enhancement
