Databases for Iris Biometric Systems: A Survey
Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris bio...
Ausführliche Beschreibung
Autor*in: |
Jan, Farmanullah [verfasserIn] Ahmed, Mohammed Imran Basheer [verfasserIn] Min-Allah, Nasro [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 1(2020), 6 vom: 08. Okt. |
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Übergeordnetes Werk: |
volume:1 ; year:2020 ; number:6 ; day:08 ; month:10 |
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DOI / URN: |
10.1007/s42979-020-00344-3 |
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Katalog-ID: |
SPR04124706X |
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520 | |a Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. | ||
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700 | 1 | |a Ahmed, Mohammed Imran Basheer |e verfasserin |4 aut | |
700 | 1 | |a Min-Allah, Nasro |e verfasserin |4 aut | |
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10.1007/s42979-020-00344-3 doi (DE-627)SPR04124706X (SPR)s42979-020-00344-3-e DE-627 ger DE-627 rakwb eng Jan, Farmanullah verfasserin aut Databases for Iris Biometric Systems: A Survey 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 Ahmed, Mohammed Imran Basheer verfasserin aut Min-Allah, Nasro verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 1(2020), 6 vom: 08. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:1 year:2020 number:6 day:08 month:10 https://dx.doi.org/10.1007/s42979-020-00344-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 AR 1 2020 6 08 10 |
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10.1007/s42979-020-00344-3 doi (DE-627)SPR04124706X (SPR)s42979-020-00344-3-e DE-627 ger DE-627 rakwb eng Jan, Farmanullah verfasserin aut Databases for Iris Biometric Systems: A Survey 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 Ahmed, Mohammed Imran Basheer verfasserin aut Min-Allah, Nasro verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 1(2020), 6 vom: 08. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:1 year:2020 number:6 day:08 month:10 https://dx.doi.org/10.1007/s42979-020-00344-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 AR 1 2020 6 08 10 |
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10.1007/s42979-020-00344-3 doi (DE-627)SPR04124706X (SPR)s42979-020-00344-3-e DE-627 ger DE-627 rakwb eng Jan, Farmanullah verfasserin aut Databases for Iris Biometric Systems: A Survey 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 Ahmed, Mohammed Imran Basheer verfasserin aut Min-Allah, Nasro verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 1(2020), 6 vom: 08. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:1 year:2020 number:6 day:08 month:10 https://dx.doi.org/10.1007/s42979-020-00344-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 AR 1 2020 6 08 10 |
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10.1007/s42979-020-00344-3 doi (DE-627)SPR04124706X (SPR)s42979-020-00344-3-e DE-627 ger DE-627 rakwb eng Jan, Farmanullah verfasserin aut Databases for Iris Biometric Systems: A Survey 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 Ahmed, Mohammed Imran Basheer verfasserin aut Min-Allah, Nasro verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 1(2020), 6 vom: 08. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:1 year:2020 number:6 day:08 month:10 https://dx.doi.org/10.1007/s42979-020-00344-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 AR 1 2020 6 08 10 |
allfieldsSound |
10.1007/s42979-020-00344-3 doi (DE-627)SPR04124706X (SPR)s42979-020-00344-3-e DE-627 ger DE-627 rakwb eng Jan, Farmanullah verfasserin aut Databases for Iris Biometric Systems: A Survey 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 Ahmed, Mohammed Imran Basheer verfasserin aut Min-Allah, Nasro verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 1(2020), 6 vom: 08. Okt. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:1 year:2020 number:6 day:08 month:10 https://dx.doi.org/10.1007/s42979-020-00344-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 AR 1 2020 6 08 10 |
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Jan, Farmanullah |
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Jan, Farmanullah misc Iris biometrics misc Near-infrared iris database misc Visible wavelength iris database misc Iris at a distance misc Iris on the move Databases for Iris Biometric Systems: A Survey |
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Databases for Iris Biometric Systems: A Survey Iris biometrics (dpeaa)DE-He213 Near-infrared iris database (dpeaa)DE-He213 Visible wavelength iris database (dpeaa)DE-He213 Iris at a distance (dpeaa)DE-He213 Iris on the move (dpeaa)DE-He213 |
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misc Iris biometrics misc Near-infrared iris database misc Visible wavelength iris database misc Iris at a distance misc Iris on the move |
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misc Iris biometrics misc Near-infrared iris database misc Visible wavelength iris database misc Iris at a distance misc Iris on the move |
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Databases for Iris Biometric Systems: A Survey |
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databases for iris biometric systems: a survey |
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Databases for Iris Biometric Systems: A Survey |
abstract |
Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. |
abstractGer |
Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. |
abstract_unstemmed |
Abstract Due to latest advances in information technology, the research community has been exploring new applications for iris biometrics. For example, it has now been integrated into the cell phones, auto teller machines, walk-through portals, etc. Literature reveals that most contemporary iris biometric systems perform poorly for image date where a subject may wear cosmetics (e.g., contact lenses), not cooperate with system, stand at far distance, and/or be on the move. Moreover, research community has also started working on subject’s recognition from video, which is a challenging task. To validate the performance of new algorithms, researchers generally need databases. If they do not find relevant databases, then they need to develop databases demanding for funds and workforce. To address this issue, this study offers an extensive survey of public databases. These databases are classified into the ideal, less-constrained, and unconstrained classes using a subjective metric based on the system’s image acquisition range, level of constrains, and noisy factors in image data. Collectively, these databases offer noisy factors such as blur, non-uniform illumination, poor contrast, eyebrows, hair, off-axis and off-angle eyes, partially open and closed eyes, artificial eyelashes and cosmetic lenses, diseased pupils, light reflections, synthetic irises, fake irises, face images captured at a distance, short videos, and so on. Images in these databases are acquired using the near infrared (NIR) and visible wavelength (VW) illumination. No doubt, the research community could comfortably use these databases to simulate the ideal and/or non-ideal iris biometric conditions. |
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title_short |
Databases for Iris Biometric Systems: A Survey |
url |
https://dx.doi.org/10.1007/s42979-020-00344-3 |
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author2 |
Ahmed, Mohammed Imran Basheer Min-Allah, Nasro |
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Ahmed, Mohammed Imran Basheer Min-Allah, Nasro |
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doi_str |
10.1007/s42979-020-00344-3 |
up_date |
2024-07-03T21:02:52.687Z |
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