Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions
Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fathee, Hala [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:118 ; year:2021 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.dsp.2021.103244 |
|---|
| Katalog-ID: |
ELV055509851 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV055509851 | ||
| 003 | DE-627 | ||
| 005 | 20230626041747.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220105s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.dsp.2021.103244 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica |
| 035 | |a (DE-627)ELV055509851 | ||
| 035 | |a (ELSEVIER)S1051-2004(21)00283-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.75 |2 bkl | ||
| 100 | 1 | |a Fathee, Hala |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| 264 | 1 | |c 2021transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. | ||
| 520 | |a Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. | ||
| 650 | 7 | |a Unconstrained environments |2 Elsevier | |
| 650 | 7 | |a Non-ideal iris recognition |2 Elsevier | |
| 650 | 7 | |a Iris datasets |2 Elsevier | |
| 650 | 7 | |a Ground truth for iris datasets |2 Elsevier | |
| 650 | 7 | |a Iris segmentation |2 Elsevier | |
| 650 | 7 | |a Non-cooperative iris segmentation |2 Elsevier | |
| 700 | 1 | |a Sahmoud, Shaaban |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academic Press |a Hill, Edward M. ELSEVIER |t Modelling SARS-CoV-2 transmission in a UK university setting |d 2021 |d a review journal |g Orlando, Fla |w (DE-627)ELV006540295 |
| 773 | 1 | 8 | |g volume:118 |g year:2021 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.dsp.2021.103244 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.75 |j Infektionskrankheiten |j parasitäre Krankheiten |x Medizin |q VZ |
| 951 | |a AR | ||
| 952 | |d 118 |j 2021 |h 0 | ||
| author_variant |
h f hf |
|---|---|
| matchkey_str |
fatheehalasahmoudshaaban:2021----:rsemnainnnoprtvadnosrieevrnetsaefhataae |
| hierarchy_sort_str |
2021transfer abstract |
| bklnumber |
44.75 |
| publishDate |
2021 |
| allfields |
10.1016/j.dsp.2021.103244 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Fathee, Hala verfasserin aut Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier Sahmoud, Shaaban oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103244 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
| spelling |
10.1016/j.dsp.2021.103244 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Fathee, Hala verfasserin aut Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier Sahmoud, Shaaban oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103244 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
| allfields_unstemmed |
10.1016/j.dsp.2021.103244 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Fathee, Hala verfasserin aut Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier Sahmoud, Shaaban oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103244 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
| allfieldsGer |
10.1016/j.dsp.2021.103244 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Fathee, Hala verfasserin aut Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier Sahmoud, Shaaban oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103244 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
| allfieldsSound |
10.1016/j.dsp.2021.103244 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Fathee, Hala verfasserin aut Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier Sahmoud, Shaaban oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103244 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
| language |
English |
| source |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:118 year:2021 pages:0 |
| sourceStr |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:118 year:2021 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Infektionskrankheiten parasitäre Krankheiten |
| institution |
findex.gbv.de |
| topic_facet |
Unconstrained environments Non-ideal iris recognition Iris datasets Ground truth for iris datasets Iris segmentation Non-cooperative iris segmentation |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| authorswithroles_txt_mv |
Fathee, Hala @@aut@@ Sahmoud, Shaaban @@oth@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
ELV006540295 |
| dewey-sort |
3610 |
| id |
ELV055509851 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055509851</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041747.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103244</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055509851</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00283-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fathee, Hala</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Unconstrained environments</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-ideal iris recognition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Iris datasets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ground truth for iris datasets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Iris segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-cooperative iris segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sahmoud, Shaaban</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:118</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103244</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">118</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Fathee, Hala |
| spellingShingle |
Fathee, Hala ddc 610 bkl 44.75 Elsevier Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| authorStr |
Fathee, Hala |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV006540295 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.75 bkl Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation Elsevier |
| topic |
ddc 610 bkl 44.75 Elsevier Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation |
| topic_unstemmed |
ddc 610 bkl 44.75 Elsevier Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation |
| topic_browse |
ddc 610 bkl 44.75 Elsevier Unconstrained environments Elsevier Non-ideal iris recognition Elsevier Iris datasets Elsevier Ground truth for iris datasets Elsevier Iris segmentation Elsevier Non-cooperative iris segmentation |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
s s ss |
| hierarchy_parent_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| hierarchy_parent_id |
ELV006540295 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV006540295 |
| title |
Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| ctrlnum |
(DE-627)ELV055509851 (ELSEVIER)S1051-2004(21)00283-9 |
| title_full |
Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| author_sort |
Fathee, Hala |
| journal |
Modelling SARS-CoV-2 transmission in a UK university setting |
| journalStr |
Modelling SARS-CoV-2 transmission in a UK university setting |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Fathee, Hala |
| container_volume |
118 |
| class |
610 VZ 44.75 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Fathee, Hala |
| doi_str_mv |
10.1016/j.dsp.2021.103244 |
| dewey-full |
610 |
| title_sort |
iris segmentation in uncooperative and unconstrained environments: state-of-the-art, datasets and future research directions |
| title_auth |
Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| abstract |
Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. |
| abstractGer |
Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. |
| abstract_unstemmed |
Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions |
| url |
https://doi.org/10.1016/j.dsp.2021.103244 |
| remote_bool |
true |
| author2 |
Sahmoud, Shaaban |
| author2Str |
Sahmoud, Shaaban |
| ppnlink |
ELV006540295 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.dsp.2021.103244 |
| up_date |
2024-07-06T17:44:53.940Z |
| _version_ |
1803852598678126592 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055509851</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041747.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103244</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055509851</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00283-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fathee, Hala</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most of the classical iris recognition systems require cooperation from users and assume ideal conditions during iris image acquisition. In uncooperative and unconstrained environments, the performance of these classical systems is significantly degraded due to the existence of various noise factors such as high occlusions, specular reflections, motion blur, lighting variations and off-angle images. Iris segmentation step has a great significance in the iris recognition system since its errors directly propagate to the next processing steps and affect the iris template as well as the recognition results. Iris segmentation step becomes harder and more challenging in uncooperative and unconstrained environments since most of those noise factors should be handled in this step. Therefore, in recent years the iris segmentation in unconstrained environments has received more attention from the scientific community. However, the state-of-the-art related to iris segmentation in unconstrained environments and its datasets is not well known. In this paper, we aim to shed the light on the most recent advances in iris segmentation in uncooperative and unconstrained environments. Furthermore, the current publicly available iris datasets and their ground truth masks that are commonly used for training or testing segmentation algorithms are examined. The limitations of existing methods and the future research directions are discussed and highlighted to assist researchers in filling the research gap for this problem domain.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Unconstrained environments</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-ideal iris recognition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Iris datasets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ground truth for iris datasets</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Iris segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-cooperative iris segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sahmoud, Shaaban</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:118</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103244</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">118</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.399454 |