Predictive text analysis using eye blinks
The current work aims to facilitate interaction with others to those with the inability to perform activities requiring motor skills or those who cannot speak. It proposes a modus operandi or a system based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM), which automaticall...
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| Veröffentlicht in: | Computers & electrical engineering 2021-12, Vol.96, p.107554, Article 107554 |
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| container_title | Computers & electrical engineering |
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| creator | Chaudhary, Gopal Lamba, Puneet Singh Jolly, Harman Singh Poply, Sakaar Khari, Manju Verdú, Elena |
| description | The current work aims to facilitate interaction with others to those with the inability to perform activities requiring motor skills or those who cannot speak. It proposes a modus operandi or a system based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM), which automatically identifies eye blinks in real-time to predict a lexicon. The system implements an auxiliary input that enables individuals to interact with others with the help of a device, where voluntary long blinks help in transition from a counter to a predictive table, while the short blinks are used to make the counter stop and select the lexicon. The system does not require prior manual initialization, special lighting, or previous face detection as it can calibrate it if the user is in the camera region and close. The proposed user interface makes the process of words detection by blinking easier with 74% accuracy.
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| doi_str_mv | 10.1016/j.compeleceng.2021.107554 |
| format | Article |
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[Display omitted]</description><subject>Blinking</subject><subject>Convolutional neural networks</subject><subject>Eye blinking</subject><subject>Eye facet correlation</subject><subject>Eye facet ratio</subject><subject>Eye gaze</subject><subject>Eye movements</subject><subject>Face recognition</subject><subject>Histogram of oriented gradients</subject><subject>Histograms</subject><subject>Support Vector Machines</subject><issn>0045-7906</issn><issn>1879-0755</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqNkEtPwzAQhC0EEqXwH4I4cUjw2_ERRbykSnCAs-W6m8ohTYqdVOTf4yocOHJazWpntPMhdE1wQTCRd03h-t0eWnDQbQuKKUl7JQQ_QQtSKp0fxSlaYMxFrjSW5-gixgYnLUm5QLdvATbeDf4A2QDfQ2Y7207Rx2yMvttmMEG2bn33GS_RWW3bCFe_c4k-Hh_eq-d89fr0Ut2vcse4HvLaCq4YVzXnglqMGZGWWiqUErV2BLjbrHkNTnBNWEklU2vBpVBWM6t1qdkS3cy5-9B_jRAH0_RjSF9FQyURnBGaUpdIz1cu9DEGqM0--J0NkyHYHMmYxvwhY45kzEwmeavZC6nGwUMw0XnoXAIRwA1m0_t_pPwAgqRwBw</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Chaudhary, Gopal</creator><creator>Lamba, Puneet Singh</creator><creator>Jolly, Harman Singh</creator><creator>Poply, Sakaar</creator><creator>Khari, Manju</creator><creator>Verdú, Elena</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202112</creationdate><title>Predictive text analysis using eye blinks</title><author>Chaudhary, Gopal ; Lamba, Puneet Singh ; Jolly, Harman Singh ; Poply, Sakaar ; Khari, Manju ; Verdú, Elena</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-fa547347f4452a00316a2a25775f9c1e4cdb4fec5491382637b54657a93a99893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Blinking</topic><topic>Convolutional neural networks</topic><topic>Eye blinking</topic><topic>Eye facet correlation</topic><topic>Eye facet ratio</topic><topic>Eye gaze</topic><topic>Eye movements</topic><topic>Face recognition</topic><topic>Histogram of oriented gradients</topic><topic>Histograms</topic><topic>Support Vector Machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chaudhary, Gopal</creatorcontrib><creatorcontrib>Lamba, Puneet Singh</creatorcontrib><creatorcontrib>Jolly, Harman Singh</creatorcontrib><creatorcontrib>Poply, Sakaar</creatorcontrib><creatorcontrib>Khari, Manju</creatorcontrib><creatorcontrib>Verdú, Elena</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & electrical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chaudhary, Gopal</au><au>Lamba, Puneet Singh</au><au>Jolly, Harman Singh</au><au>Poply, Sakaar</au><au>Khari, Manju</au><au>Verdú, Elena</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive text analysis using eye blinks</atitle><jtitle>Computers & electrical engineering</jtitle><date>2021-12</date><risdate>2021</risdate><volume>96</volume><spage>107554</spage><pages>107554-</pages><artnum>107554</artnum><issn>0045-7906</issn><eissn>1879-0755</eissn><abstract>The current work aims to facilitate interaction with others to those with the inability to perform activities requiring motor skills or those who cannot speak. It proposes a modus operandi or a system based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM), which automatically identifies eye blinks in real-time to predict a lexicon. The system implements an auxiliary input that enables individuals to interact with others with the help of a device, where voluntary long blinks help in transition from a counter to a predictive table, while the short blinks are used to make the counter stop and select the lexicon. The system does not require prior manual initialization, special lighting, or previous face detection as it can calibrate it if the user is in the camera region and close. The proposed user interface makes the process of words detection by blinking easier with 74% accuracy.
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| fulltext | fulltext |
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| ispartof | Computers & electrical engineering, 2021-12, Vol.96, p.107554, Article 107554 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Blinking Convolutional neural networks Eye blinking Eye facet correlation Eye facet ratio Eye gaze Eye movements Face recognition Histogram of oriented gradients Histograms Support Vector Machines |
| title | Predictive text analysis using eye blinks |
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