{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:52:46Z","timestamp":1772265166591,"version":"3.50.1"},"posted":{"date-parts":[[2019,1,15]]},"group-title":"PeerJ Preprints","reference-count":0,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T00:00:00Z","timestamp":1547510400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition.<\/jats:p>\n                <jats:p>In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.<\/jats:p>","DOI":"10.7287\/peerj.preprints.27363v2","type":"posted-content","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T14:21:06Z","timestamp":1547562066000},"source":"Crossref","is-referenced-by-count":1,"title":["A fast iris recognition system through optimum feature extraction"],"prefix":"10.7287","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7834-0847","authenticated-orcid":true,"given":"Humayan Kabir","family":"Rana","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh"}]},{"given":"Md. Shafiul","family":"Azam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh"}]},{"given":"Mst. Rashida","family":"Akhtar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9674-9646","authenticated-orcid":true,"given":"Julian M.W.","family":"Quinn","sequence":"additional","affiliation":[{"name":"Bone Biology Division, Garvan Institute of Medical Research, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":true,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"School of Biomedical Science, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia"}]}],"member":"4443","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/peerj.com\/preprints\/27363v2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/27363v2.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/27363v2.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/27363v2.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T20:02:29Z","timestamp":1577131349000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/preprints\/27363v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7287\/peerj.preprints.27363v2","relation":{"references":[{"id-type":"doi","id":"10.7287\/peerj.preprints.27363v2\/supp-1","asserted-by":"subject"},{"id-type":"doi","id":"10.7287\/peerj.preprints.27363v2\/supp-1","asserted-by":"object"}],"is-replaced-by":[{"id-type":"doi","id":"10.7287\/peerj.preprints.27363v3","asserted-by":"subject"}],"is-original-form-of":[{"id-type":"doi","id":"10.7287\/peerj.preprints.27363v3","asserted-by":"object"}],"replaces":[{"id-type":"doi","id":"10.7287\/peerj.preprints.27363v1","asserted-by":"object"}]},"subject":[],"published":{"date-parts":[[2019,1,15]]},"subtype":"preprint"}}