Designing a Roll Call System with Facial Recognition on Kubeflow

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971602

Winggun Wong, Meng-Yuan Tsai, Hung-Kuei Chang

引用次数: 0

Abstract

This study is based on the Kubeflow machine learning development platform in order to deploy a real-time roll call system. Kubeflow is based on Kubernetes, which is convenient for container management and portability. Face recognition is done in three steps. First, MTCNN detects a face in the image. Then, FaceNet extracts the features from the face. Finally, SVM finds out the identity of the face closest to the detected face. The average accuracy of the 30 classes in this study is approximately 94.2%, and the execution speed is about 35fps, with Intel Core i7-10700 CPU and NVIDIA GeForce RTX 3060 GPU.

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基于Kubeflow的人脸识别点名系统设计

本研究基于Kubeflow机器学习开发平台，以部署一个实时点名系统。Kubeflow基于Kubernetes，它便于容器管理和可移植性。人脸识别分三步完成。首先，MTCNN在图像中检测人脸。然后，FaceNet从人脸中提取特征。最后，SVM找出与被检测人脸最接近的人脸的身份。本研究中30个类的平均准确率约为94.2%，执行速度约为35fps, CPU为Intel Core i7-10700, GPU为NVIDIA GeForce RTX 3060。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.