Image authenticity implementing Principal Component Analysis (PCA)

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.