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The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal\u2010Component Analysis\u2010 (PCA\u2010) and Independent\u2010Component Analysis\u2010 (ICA\u2010) based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278\u2009bits for PCA and 288\u2009bits for ICA iris features using Masek\u2032s iris recognition scheme. This value approximately matches previous estimates of iris information content.<\/jats:p>","DOI":"10.1155\/2012\/282589","type":"journal-article","created":{"date-parts":[[2012,7,27]],"date-time":"2012-07-27T03:11:43Z","timestamp":1343358703000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images"],"prefix":"10.1155","volume":"2012","author":[{"given":"R.","family":"Youmaran","sequence":"first","affiliation":[]},{"given":"A.","family":"Adler","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2012,7,26]]},"reference":[{"key":"e_1_2_6_1_2","unstructured":"WaymanJ. S. 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