Iris recognition based on a novel variation of local binary pattern

Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel an...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Chengcheng [verfasserIn] Zhou, Weidong [verfasserIn] Yuan, Shasha [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Iris recognition Average local binary pattern NN classifier SVM classifier

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 31(2014), 10 vom: 27. Sept., Seite 1419-1429
Übergeordnetes Werk:	volume:31 ; year:2014 ; number:10 ; day:27 ; month:09 ; pages:1419-1429

Links:	Volltext

DOI / URN:	10.1007/s00371-014-1023-5

Katalog-ID:	SPR004679075

Internformat


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520			\|a Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases.
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700	1		\|a Yuan, Shasha \|e verfasserin \|4 aut
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912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
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912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2021
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912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
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912			\|a GBV_ILN_2038
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912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2065
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912			\|a GBV_ILN_2093
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912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
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912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
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allfields	10.1007/s00371-014-1023-5 doi (DE-627)SPR004679075 (SPR)s00371-014-1023-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Li, Chengcheng verfasserin aut Iris recognition based on a novel variation of local binary pattern 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases. Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213 Zhou, Weidong verfasserin aut Yuan, Shasha verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 31(2014), 10 vom: 27. Sept., Seite 1419-1429 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429 https://dx.doi.org/10.1007/s00371-014-1023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 31 2014 10 27 09 1419-1429
spelling	10.1007/s00371-014-1023-5 doi (DE-627)SPR004679075 (SPR)s00371-014-1023-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Li, Chengcheng verfasserin aut Iris recognition based on a novel variation of local binary pattern 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases. Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213 Zhou, Weidong verfasserin aut Yuan, Shasha verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 31(2014), 10 vom: 27. Sept., Seite 1419-1429 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429 https://dx.doi.org/10.1007/s00371-014-1023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 31 2014 10 27 09 1419-1429
allfields_unstemmed	10.1007/s00371-014-1023-5 doi (DE-627)SPR004679075 (SPR)s00371-014-1023-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Li, Chengcheng verfasserin aut Iris recognition based on a novel variation of local binary pattern 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases. Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213 Zhou, Weidong verfasserin aut Yuan, Shasha verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 31(2014), 10 vom: 27. Sept., Seite 1419-1429 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429 https://dx.doi.org/10.1007/s00371-014-1023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 31 2014 10 27 09 1419-1429
allfieldsGer	10.1007/s00371-014-1023-5 doi (DE-627)SPR004679075 (SPR)s00371-014-1023-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Li, Chengcheng verfasserin aut Iris recognition based on a novel variation of local binary pattern 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases. Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213 Zhou, Weidong verfasserin aut Yuan, Shasha verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 31(2014), 10 vom: 27. Sept., Seite 1419-1429 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429 https://dx.doi.org/10.1007/s00371-014-1023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 31 2014 10 27 09 1419-1429
allfieldsSound	10.1007/s00371-014-1023-5 doi (DE-627)SPR004679075 (SPR)s00371-014-1023-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Li, Chengcheng verfasserin aut Iris recognition based on a novel variation of local binary pattern 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases. Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213 Zhou, Weidong verfasserin aut Yuan, Shasha verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 31(2014), 10 vom: 27. Sept., Seite 1419-1429 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429 https://dx.doi.org/10.1007/s00371-014-1023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 31 2014 10 27 09 1419-1429
language	English
source	Enthalten in The visual computer 31(2014), 10 vom: 27. Sept., Seite 1419-1429 volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429
sourceStr	Enthalten in The visual computer 31(2014), 10 vom: 27. Sept., Seite 1419-1429 volume:31 year:2014 number:10 day:27 month:09 pages:1419-1429
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Iris recognition Average local binary pattern NN classifier SVM classifier
dewey-raw	004
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container_title	The visual computer
authorswithroles_txt_mv	Li, Chengcheng @@aut@@ Zhou, Weidong @@aut@@ Yuan, Shasha @@aut@@
publishDateDaySort_date	2014-09-27T00:00:00Z
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language_de	englisch
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author	Li, Chengcheng
spellingShingle	Li, Chengcheng ddc 004 bkl 54.73 misc Iris recognition misc Average local binary pattern misc NN classifier misc SVM classifier Iris recognition based on a novel variation of local binary pattern
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topic_title	004 ASE 54.73 bkl Iris recognition based on a novel variation of local binary pattern Iris recognition (dpeaa)DE-He213 Average local binary pattern (dpeaa)DE-He213 NN classifier (dpeaa)DE-He213 SVM classifier (dpeaa)DE-He213
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title	Iris recognition based on a novel variation of local binary pattern
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title_full	Iris recognition based on a novel variation of local binary pattern
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title_sort	iris recognition based on a novel variation of local binary pattern
title_auth	Iris recognition based on a novel variation of local binary pattern
abstract	Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases.
abstractGer	Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases.
abstract_unstemmed	Abstract In this paper, an efficient method based on a novel variation of local binary pattern (LBP), average local binary pattern (ALBP), is proposed for iris recognition, which is less sensitive to histogram equalization and parameters’ selection and has low computation complexity. Center pixel and its neighborhood are the crucial elements involved in basic LBP. ALBP places high value on the significance of center pixel, while nearly all other variations of LBP have been focusing on the selection of neighborhood. Four candidates for the modification of the center pixel are elected and validated, respectively. In the proposed framework, the valid iris region firstly is localized and then normalized into a uniform rectangular. Then the normalized iris is chopped into several sub-images, and ALBP operator is applied to each sub-image to obtain individual histogram feature. Every histogram feature is then concatenated to form a global iris feature vector. Nearest neighbor classifier and support vector machine are employed to validate the recognition performance. Experimental results on CASIA-IrisV4 (including CASIA-Iris-Interval and CASIA-Iris-Thousand) and UBIRIS.V1 database show that our method achieves competitive recognition performance (optimal recognition rate is %$99.91\,\%%$) compared with other methods using the same databases.
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container_issue	10
title_short	Iris recognition based on a novel variation of local binary pattern
url	https://dx.doi.org/10.1007/s00371-014-1023-5
remote_bool	true
author2	Zhou, Weidong Yuan, Shasha
author2Str	Zhou, Weidong Yuan, Shasha
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hochschulschrift_bool	false
doi_str	10.1007/s00371-014-1023-5
up_date	2024-07-04T02:09:38.819Z
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