Proposed big data architecture for facial recognition using machine learning
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‑processi...
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-02-01
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| Series: | AIMS Electronics and Electrical Engineering |
| Subjects: | |
| Online Access: | http://awstest.aimspress.com/article/doi/10.3934/electreng.2021005?viewType=HTML |
| _version_ | 1818648212502740992 |
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| author | Suriya Priya R Asaithambi Sitalakshmi Venkatraman Ramanathan Venkatraman |
| author_facet | Suriya Priya R Asaithambi Sitalakshmi Venkatraman Ramanathan Venkatraman |
| author_sort | Suriya Priya R Asaithambi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T01:14:50Z |
| format | Article |
| id | doaj.art-885570769fa8458db4a66b79a8357a67 |
| institution | Directory Open Access Journal |
| issn | 2578-1588 |
| language | English |
| last_indexed | 2024-12-17T01:14:50Z |
| publishDate | 2021-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj.art-885570769fa8458db4a66b79a8357a672022-12-21T22:09:01ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882021-02-0151689210.3934/electreng.2021005Proposed big data architecture for facial recognition using machine learningSuriya Priya R Asaithambi0Sitalakshmi Venkatraman1Ramanathan Venkatraman 21. Institute of Systems Science, National University of Singapore, Singapore2. Department of Information Technology, Melbourne Polytechnic, VIC, Australia1. Institute of Systems Science, National University of Singapore, SingaporeWith 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.http://awstest.aimspress.com/article/doi/10.3934/electreng.2021005?viewType=HTMLbig datamachine learningsocial networkssentiment analysisfacial recognitiondistributed computing |
| spellingShingle | Suriya Priya R Asaithambi Sitalakshmi Venkatraman Ramanathan Venkatraman Proposed big data architecture for facial recognition using machine learning AIMS Electronics and Electrical Engineering big data machine learning social networks sentiment analysis facial recognition distributed computing |
| title | Proposed big data architecture for facial recognition using machine learning |
| title_full | Proposed big data architecture for facial recognition using machine learning |
| title_fullStr | Proposed big data architecture for facial recognition using machine learning |
| title_full_unstemmed | Proposed big data architecture for facial recognition using machine learning |
| title_short | Proposed big data architecture for facial recognition using machine learning |
| title_sort | proposed big data architecture for facial recognition using machine learning |
| topic | big data machine learning social networks sentiment analysis facial recognition distributed computing |
| url | http://awstest.aimspress.com/article/doi/10.3934/electreng.2021005?viewType=HTML |
| work_keys_str_mv | AT suriyapriyarasaithambi proposedbigdataarchitectureforfacialrecognitionusingmachinelearning AT sitalakshmivenkatraman proposedbigdataarchitectureforfacialrecognitionusingmachinelearning AT ramanathanvenkatraman proposedbigdataarchitectureforfacialrecognitionusingmachinelearning |