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Proposed Big Data Architecture for Facial Recognition Using Machine Learning

AIMS Electronics and Electrical Engineering(2021)

1. Institute of Systems Science | 2. Department of Information Technology

Cited 7|Views11
Abstract
With the abundance of raw data generated from various sources including social networks, big data has become essential in acquiring, processing, and analyzing heterogeneous data from multiple sources for real-time applications. In this paper, we propose a big data framework suitable for pre‑processing and classification of image as well as text analytics by employing two key workflows, called big data (BD) pipeline and machine learning (ML) pipeline. Our unique end-to-end workflow integrates data cleansing, data integration, data transformation and data reduction processes, followed by various analytics using suitable machine learning techniques. Further, our model is the first of its kind to augment facial recognition with sentiment analysis in a distributed big data framework. The implementation of our model uses state-of-the-art distributed technologies to ingest, prepare, process and analyze big data for generating actionable data insights by employing relevant ML algorithms such as k-NN, logistic regression and decision tree. In addition, we demonstrate the application of our big data framework to facial recognition system using open sources by developing a prototype as a use case. We also employ sentiment analysis on non-repetitive semi structured public data (text) such as user comments, image tagging, and other information associated with the facial images. We believe our work provides a novel approach to intersect Big Data, ML and Face Recognition and would create new research to alleviate some of the challenges associated with big data processing in real world applications.
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Key words
big data,machine learning,social networks,sentiment analysis,facial recognition,distributed computing
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要点】:本文提出了一个大数据处理框架,通过集成数据清洗、数据整合、数据转换和数据简化过程,并结合机器学习技术,实现了面部识别与情感分析的融合,为大数据环境下的实时应用提供了创新解决方案。

方法】:研究采用了一个端到端的流程,包括大数据管道(BD pipeline)和机器学习管道(ML pipeline),利用分布式技术进行数据预处理和分析,并应用k-NN、逻辑回归和决策树等机器学习算法。

实验】:研究者开发了原型以验证所提出的大数据框架在面部识别系统中的应用,使用了开源技术,并对非重复半结构化的公开数据(如用户评论、图像标签等)进行了情感分析,实验结果展示了框架的有效性和可行性。数据集名称在文中未明确提及。