NDLI: Image authenticity implementing Principal Component Analysis (PCA)

Content Provider	IEEE Xplore Digital Library
Author	Schmeelk, S. Schmeelk, J.
Copyright Year	2013
Description	Author affiliation: Math. Dept., VCUQ, Doha, Qatar (Schmeelk, J.) \|\| Cyber Security Dept., LGS Bell Labs. Innovations LLC, Florham Park, NJ, USA (Schmeelk, S.)
Abstract	The paper addresses the application of finding key features within an image utilizing the process termed the Principal Components Analysis (PCA). Understanding this technique is critical for researchers within biometric fields and the larger cyber security field. Research, found in ASEE 2011 Conference Proceedings, titled “Edge Detectors in Engineering and Medical Applications,” develops the identification of edges within an image. That paper and this paper give the user two alternate approaches for comparing images. The PCA method was selected for analysis because it requires the use of many mathematical and statistical processes, such as means, standard deviation, variance, covariance, and eigenvalues, leading to a feature vector to compare images. The plan is to identify images, which will be termed authentic images and imposter images. Then the authentic and imposter images will be measured by th Euclidean norm to determine their authenticity. Developing software engineers and/or applied mathematicians using eigenvalues of a matrix can identify the authenticity of an image via that of an imposter image. This paper develops the key mathematical requirements to obtain a feature vector for a particular image.
Starting Page	1
Ending Page	4
File Size	677936
Page Count	4
File Format	PDF
ISBN	9781479925469
DOI	10.1109/CEWIT.2013.6713751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics Image edge detection Gray-scale Vectors Covariance matrices Standards Covariance Matrices Feature Vectors Eigenvalues Eigenvalues and eigenfunctions Eigenvectors Relative Operating Characterists Principal Component Analysis Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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