Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm
Pattern recognition problem rely upon the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Sree, P. Kiran Babu, I. Ramesh |
| description | Pattern recognition problem rely upon the features inherent in the pattern of
images. Face detection and recognition is one of the challenging research areas
in the field of computer vision. In this paper, we present a method to identify
skin pixels from still and video images using skin color. Face regions are
identified from this skin pixel region. Facial features such as eyes, nose and
mouth are then located. Faces are recognized from color images using an RBF
based neural network. Unsupervised Cellular Automata with K means clustering
algorithm is used to locate different facial elements. Orientation is corrected
by using eyes. Parameters like inter eye distance, nose length, mouth position,
Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a
Radial Basis Function (RBF) based neural network. This approach reliably works
for face sequence with orientation in head, expressions etc. |
| doi_str_mv | 10.48550/arxiv.1312.6834 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1312_6834</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1312_6834</sourcerecordid><originalsourceid>FETCH-LOGICAL-a654-c0d55ef6808073b60e9c57c35c6c7711a6f3188ff7b6f5a8f8e2f4ee635fe8553</originalsourceid><addsrcrecordid>eNotzzFPwzAUBGAvDKiwM6H3BxKSOnbMWAUKFZVYCmv06jwHS3ZSbKfAv6ehTDec7qSPsZuyyCslRHGH4dse85KXy1wqXl2yzzVqggdKpJMdBzBh9BCTdQ5w6ODddjTCxmNPEaZohx7ehjgdKBxtpA4acm5yGGA1pdFjQviy6QNewBMOEbSbYqIwz9D1Yzh1_opdGHSRrv9zwXbrx13znG1fnzbNapuhFFWmi04IMlIVqqj5XhZ0r0WtudBS13VZojS8VMqYei-NQGUULU1FJLkwdKLyBbs93_6R20OwHsNPO9Pbmc5_AdEnVcU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm</title><source>arXiv.org</source><creator>Sree, P. Kiran ; Babu, I. Ramesh</creator><creatorcontrib>Sree, P. Kiran ; Babu, I. Ramesh</creatorcontrib><description>Pattern recognition problem rely upon the features inherent in the pattern of
images. Face detection and recognition is one of the challenging research areas
in the field of computer vision. In this paper, we present a method to identify
skin pixels from still and video images using skin color. Face regions are
identified from this skin pixel region. Facial features such as eyes, nose and
mouth are then located. Faces are recognized from color images using an RBF
based neural network. Unsupervised Cellular Automata with K means clustering
algorithm is used to locate different facial elements. Orientation is corrected
by using eyes. Parameters like inter eye distance, nose length, mouth position,
Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a
Radial Basis Function (RBF) based neural network. This approach reliably works
for face sequence with orientation in head, expressions etc.</description><identifier>DOI: 10.48550/arxiv.1312.6834</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2013-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1312.6834$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1312.6834$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Sree, P. Kiran</creatorcontrib><creatorcontrib>Babu, I. Ramesh</creatorcontrib><title>Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm</title><description>Pattern recognition problem rely upon the features inherent in the pattern of
images. Face detection and recognition is one of the challenging research areas
in the field of computer vision. In this paper, we present a method to identify
skin pixels from still and video images using skin color. Face regions are
identified from this skin pixel region. Facial features such as eyes, nose and
mouth are then located. Faces are recognized from color images using an RBF
based neural network. Unsupervised Cellular Automata with K means clustering
algorithm is used to locate different facial elements. Orientation is corrected
by using eyes. Parameters like inter eye distance, nose length, mouth position,
Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a
Radial Basis Function (RBF) based neural network. This approach reliably works
for face sequence with orientation in head, expressions etc.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzzFPwzAUBGAvDKiwM6H3BxKSOnbMWAUKFZVYCmv06jwHS3ZSbKfAv6ehTDec7qSPsZuyyCslRHGH4dse85KXy1wqXl2yzzVqggdKpJMdBzBh9BCTdQ5w6ODddjTCxmNPEaZohx7ehjgdKBxtpA4acm5yGGA1pdFjQviy6QNewBMOEbSbYqIwz9D1Yzh1_opdGHSRrv9zwXbrx13znG1fnzbNapuhFFWmi04IMlIVqqj5XhZ0r0WtudBS13VZojS8VMqYei-NQGUULU1FJLkwdKLyBbs93_6R20OwHsNPO9Pbmc5_AdEnVcU</recordid><startdate>20131215</startdate><enddate>20131215</enddate><creator>Sree, P. Kiran</creator><creator>Babu, I. Ramesh</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20131215</creationdate><title>Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm</title><author>Sree, P. Kiran ; Babu, I. Ramesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a654-c0d55ef6808073b60e9c57c35c6c7711a6f3188ff7b6f5a8f8e2f4ee635fe8553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Sree, P. Kiran</creatorcontrib><creatorcontrib>Babu, I. Ramesh</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sree, P. Kiran</au><au>Babu, I. Ramesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm</atitle><date>2013-12-15</date><risdate>2013</risdate><abstract>Pattern recognition problem rely upon the features inherent in the pattern of
images. Face detection and recognition is one of the challenging research areas
in the field of computer vision. In this paper, we present a method to identify
skin pixels from still and video images using skin color. Face regions are
identified from this skin pixel region. Facial features such as eyes, nose and
mouth are then located. Faces are recognized from color images using an RBF
based neural network. Unsupervised Cellular Automata with K means clustering
algorithm is used to locate different facial elements. Orientation is corrected
by using eyes. Parameters like inter eye distance, nose length, mouth position,
Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a
Radial Basis Function (RBF) based neural network. This approach reliably works
for face sequence with orientation in head, expressions etc.</abstract><doi>10.48550/arxiv.1312.6834</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.1312.6834 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_1312_6834 |
| source | arXiv.org |
| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T13%3A06%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Face%20Detection%20from%20still%20and%20Video%20Images%20using%20Unsupervised%20Cellular%20Automata%20with%20K%20means%20clustering%20algorithm&rft.au=Sree,%20P.%20Kiran&rft.date=2013-12-15&rft_id=info:doi/10.48550/arxiv.1312.6834&rft_dat=%3Carxiv_GOX%3E1312_6834%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |