An Implementation of Face Recognition with Deep Learning based on a Container-Orchestration Platform

2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) Pub Date : 2020-02-01 DOI:10.1109/Indo-TaiwanICAN48429.2020.9181343

Winggun Wong, Cheng-Sheng Lee

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引用次数: 1

Abstract

As a lightweight alternative to a virtual machine, a container runs applications only with the necessary environmental variables, libraries, etc. Moreover, many more containers can be run on the same computer compared to traditional VMs, which take up a lot of computing resources. Currently, Docker container and Kubernetes (K8s), which is a container-orchestration platform, are very popular tools. In addition, K8s is a high availability (HA) system with many features that can provide containers to implement more applications. In this project, a face recognition application is implemented with deep learning on Kubeflow, which is a machine learning platform running on K8s. Also, the deep learning method output features instead of classifications. This method computes the distance between two images with Triplet loss function and Euclidean distance. K8s runs on the server as a private cloud, on which our face recognition application runs.

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基于容器编排平台的深度学习人脸识别实现

作为虚拟机的轻量级替代品，容器只运行带有必要环境变量、库等的应用程序。此外，与占用大量计算资源的传统vm相比，在同一台计算机上可以运行更多的容器。目前，Docker容器和Kubernetes (k8)是非常流行的工具，Kubernetes是一个容器编排平台。此外，K8s是一个高可用性(HA)系统，具有许多特性，可以提供容器来实现更多应用程序。本项目在Kubeflow上使用深度学习实现了一个人脸识别应用，Kubeflow是一个运行在k8上的机器学习平台。此外，深度学习方法输出特征而不是分类。该方法利用三元损失函数和欧氏距离计算两幅图像之间的距离。K8s作为私有云在服务器上运行，我们的人脸识别应用程序就运行在私有云上。

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

2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)

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