000			03456cam a2200493Ki 4500
001			9781003110620
003			FlBoTFG
005			20240213122824.0
006			m o d
007			cr cnu|||unuuu
008			211007s2021 xx o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003110620 _q(electronic bk.)
020			_a1003110622 _q(electronic bk.)
020			_z9780367627508
020			_a9781000508192 _q(electronic bk. : PDF)
020			_a1000508196 _q(electronic bk. : PDF)
020			_a9781000508215 _q(electronic bk. : EPUB)
020			_a1000508218 _q(electronic bk. : EPUB)
020			_z9780367627478
035			_a(OCoLC)1273727942
035			_a(OCoLC-P)1273727942
050		4	_aTK7882.B56 _bZ356 2021
072		7	_aCOM _x051300 _2bisacsh
072		7	_aUY _2bicssc
082	0	4	_a006.4 _223
100	1		_aZainal Abidin, Zaheera.
245	1	0	_aSwarm Intelligence for Iris Recognition.
250			_aFirst edition.
264		1	_a[Place of publication not identified] : _bCRC Press, _c2021.
300			_a1 online resource (x, 136 pages).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_a1. Introduction 2. Human Eye 3. The First Phase of Iris Recognition 4. The Second Phase of Iris Recognition 5. Swarm-Inspired Iris Recognition 6. Conclusion
520			_aIris recognition is one of the highest accuracy techniques used in biometric systems. The accuracy of the iris recognition system is measured by False Reject Rate (FRR), which measures the authenticity of a user who is incorrectly rejected by the system due to changes in iris features (such as aging and health condition) and external factors that affect iris image, for instance, high noise rate. External factors such as technical fault, occlusion, and source of lighting that causes the image acquisition to produce distorted iris images create error, hence are incorrectly rejected by the biometric system. FRR can be reduced using wavelets and Gabor filters, cascaded classifiers, ordinal measures, multiple biometric modalities, and a selection of unique iris features. Nonetheless, in the long duration of the matching process, existing methods were unable to identify the authenticity of the user since the iris structure itself produces a template changed due to aging. In fact, the iris consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles, and pupils that are distinguishable among humans. Earlier research was done by selecting unique iris features. However, these had low accuracy levels. A new way of identifying and matching the iris template using the nature-inspired algorithm is described in this book. It provides an overview of iris recognition that is based on nature-inspired environment technology. The book is useful for students from universities, polytechnics, community colleges; practitioners; and industry practitioners.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aBiometric identification _xTechnological innovations.
650		0	_aIris (Eye)
650		0	_aSwarm intelligence.
650		7	_aCOMPUTERS / Programming / Algorithms _2bisacsh
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003110620
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c4803 _d4803