Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as fa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:SN applied sciences 2021, Vol.3 (1), p.19, Article 19
Hauptverfasser: Mousavi, Seyed Muhammad Hossein, Mirinezhad, S. Younes
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page 19
container_title SN applied sciences
container_volume 3
creator Mousavi, Seyed Muhammad Hossein
Mirinezhad, S. Younes
description This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.
doi_str_mv 10.1007/s42452-020-03999-y
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2788426862</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2788426862</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-7a750a20b4b8ef6bb1c56f09cccf77078ae666410a37ed2821006af1acb7157e3</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EElXpD7CyxAYWLmM7sRN2vAoVldjA2po4NrSUpNgpUv4el_KQWLCa0cy9ZzSXkEMOYw6gT2MmslwwEMBAlmXJ-h0yELmQTJaa7_70Su6TUYwLABC6lFkhB8RPAzZzbOjLvHG2ox6tozV2WGF09Hh6N7m6ODmjSG27bAOr3ap7pptd_Uea9ssESPOq_4a9jwWNroltOCB7HpfRjb7qkDxOrh8ub9ns_mZ6eT5jVirZMY06BxRQZVXhvKoqbnPlobTWeq1BF-iUUhkHlNrVohDpf4Weo600z7WTQ3K05a5C-7Z2sTOLdh2adNIIXRSZUIUSSSW2KhvaGIPzZhXmrxh6w8FsIjXbSE2K1HxGavpkkltTTOLmyYVf9D-uD9MmeGQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2788426862</pqid></control><display><type>article</type><title>Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor</title><source>DOAJ Directory of Open Access Journals</source><source>Springer Nature OA Free Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Mousavi, Seyed Muhammad Hossein ; Mirinezhad, S. Younes</creator><creatorcontrib>Mousavi, Seyed Muhammad Hossein ; Mirinezhad, S. Younes</creatorcontrib><description>This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.</description><identifier>ISSN: 2523-3963</identifier><identifier>EISSN: 2523-3971</identifier><identifier>DOI: 10.1007/s42452-020-03999-y</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Age determination ; Algorithms ; Animation ; Applied and Technical Physics ; Artificial neural networks ; Chemistry/Food Science ; Chronology ; Color ; Color imagery ; Data acquisition ; Deep learning ; Depth perception ; Earth Sciences ; Engineering ; Engineering: Digital Image Processing ; Environment ; Face ; Face recognition ; Feature extraction ; Females ; Image processing ; Machine learning ; Materials Science ; Multilayers ; Neural networks ; Pattern recognition ; Psychology ; Research Article ; Sensors ; Support vector machines</subject><ispartof>SN applied sciences, 2021, Vol.3 (1), p.19, Article 19</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-7a750a20b4b8ef6bb1c56f09cccf77078ae666410a37ed2821006af1acb7157e3</citedby><cites>FETCH-LOGICAL-c363t-7a750a20b4b8ef6bb1c56f09cccf77078ae666410a37ed2821006af1acb7157e3</cites><orcidid>0000-0001-6906-2152</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s42452-020-03999-y$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://doi.org/10.1007/s42452-020-03999-y$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,862,27907,27908,41103,42172,51559</link.rule.ids></links><search><creatorcontrib>Mousavi, Seyed Muhammad Hossein</creatorcontrib><creatorcontrib>Mirinezhad, S. Younes</creatorcontrib><title>Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor</title><title>SN applied sciences</title><addtitle>SN Appl. Sci</addtitle><description>This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.</description><subject>Age determination</subject><subject>Algorithms</subject><subject>Animation</subject><subject>Applied and Technical Physics</subject><subject>Artificial neural networks</subject><subject>Chemistry/Food Science</subject><subject>Chronology</subject><subject>Color</subject><subject>Color imagery</subject><subject>Data acquisition</subject><subject>Deep learning</subject><subject>Depth perception</subject><subject>Earth Sciences</subject><subject>Engineering</subject><subject>Engineering: Digital Image Processing</subject><subject>Environment</subject><subject>Face</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Females</subject><subject>Image processing</subject><subject>Machine learning</subject><subject>Materials Science</subject><subject>Multilayers</subject><subject>Neural networks</subject><subject>Pattern recognition</subject><subject>Psychology</subject><subject>Research Article</subject><subject>Sensors</subject><subject>Support vector machines</subject><issn>2523-3963</issn><issn>2523-3971</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtOwzAQRS0EElXpD7CyxAYWLmM7sRN2vAoVldjA2po4NrSUpNgpUv4el_KQWLCa0cy9ZzSXkEMOYw6gT2MmslwwEMBAlmXJ-h0yELmQTJaa7_70Su6TUYwLABC6lFkhB8RPAzZzbOjLvHG2ox6tozV2WGF09Hh6N7m6ODmjSG27bAOr3ap7pptd_Uea9ssESPOq_4a9jwWNroltOCB7HpfRjb7qkDxOrh8ub9ns_mZ6eT5jVirZMY06BxRQZVXhvKoqbnPlobTWeq1BF-iUUhkHlNrVohDpf4Weo600z7WTQ3K05a5C-7Z2sTOLdh2adNIIXRSZUIUSSSW2KhvaGIPzZhXmrxh6w8FsIjXbSE2K1HxGavpkkltTTOLmyYVf9D-uD9MmeGQ</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Mousavi, Seyed Muhammad Hossein</creator><creator>Mirinezhad, S. Younes</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-6906-2152</orcidid></search><sort><creationdate>2021</creationdate><title>Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor</title><author>Mousavi, Seyed Muhammad Hossein ; Mirinezhad, S. Younes</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-7a750a20b4b8ef6bb1c56f09cccf77078ae666410a37ed2821006af1acb7157e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age determination</topic><topic>Algorithms</topic><topic>Animation</topic><topic>Applied and Technical Physics</topic><topic>Artificial neural networks</topic><topic>Chemistry/Food Science</topic><topic>Chronology</topic><topic>Color</topic><topic>Color imagery</topic><topic>Data acquisition</topic><topic>Deep learning</topic><topic>Depth perception</topic><topic>Earth Sciences</topic><topic>Engineering</topic><topic>Engineering: Digital Image Processing</topic><topic>Environment</topic><topic>Face</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Females</topic><topic>Image processing</topic><topic>Machine learning</topic><topic>Materials Science</topic><topic>Multilayers</topic><topic>Neural networks</topic><topic>Pattern recognition</topic><topic>Psychology</topic><topic>Research Article</topic><topic>Sensors</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mousavi, Seyed Muhammad Hossein</creatorcontrib><creatorcontrib>Mirinezhad, S. Younes</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database (ProQuest)</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>SN applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mousavi, Seyed Muhammad Hossein</au><au>Mirinezhad, S. Younes</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor</atitle><jtitle>SN applied sciences</jtitle><stitle>SN Appl. Sci</stitle><date>2021</date><risdate>2021</risdate><volume>3</volume><issue>1</issue><spage>19</spage><pages>19-</pages><artnum>19</artnum><issn>2523-3963</issn><eissn>2523-3971</eissn><abstract>This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s42452-020-03999-y</doi><orcidid>https://orcid.org/0000-0001-6906-2152</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2523-3963
ispartof SN applied sciences, 2021, Vol.3 (1), p.19, Article 19
issn 2523-3963
2523-3971
language eng
recordid cdi_proquest_journals_2788426862
source DOAJ Directory of Open Access Journals; Springer Nature OA Free Journals; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
subjects Age determination
Algorithms
Animation
Applied and Technical Physics
Artificial neural networks
Chemistry/Food Science
Chronology
Color
Color imagery
Data acquisition
Deep learning
Depth perception
Earth Sciences
Engineering
Engineering: Digital Image Processing
Environment
Face
Face recognition
Feature extraction
Females
Image processing
Machine learning
Materials Science
Multilayers
Neural networks
Pattern recognition
Psychology
Research Article
Sensors
Support vector machines
title Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T04%3A15%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Iranian%20kinect%20face%20database%20(IKFDB):%20a%20color-depth%20based%20face%20database%20collected%20by%20kinect%20v.2%20sensor&rft.jtitle=SN%20applied%20sciences&rft.au=Mousavi,%20Seyed%20Muhammad%20Hossein&rft.date=2021&rft.volume=3&rft.issue=1&rft.spage=19&rft.pages=19-&rft.artnum=19&rft.issn=2523-3963&rft.eissn=2523-3971&rft_id=info:doi/10.1007/s42452-020-03999-y&rft_dat=%3Cproquest_cross%3E2788426862%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2788426862&rft_id=info:pmid/&rfr_iscdi=true