{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:02Z","timestamp":1777704542717,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T00:00:00Z","timestamp":1532736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,11,20]]},"abstract":"<jats:p>\n                    Eye blink is a semi-autonomic rapid closing of eyelid. Eye blink is controlled by the autonomic nervous system of human brain. This normal and most important function of human eye can be embedded with some created (abnormal) sequences to indicate any unusual message. In this paper, a real time novel algorithm to reckon number of eye blinks (R\n                    <jats:sub>T<\/jats:sub>\n                    REB) in a video sequence using eye facet correlation (EFC) is proposed. The proposed R\n                    <jats:sub>T<\/jats:sub>\n                    REB approximates the eye landmark positions using EFC- an extricate variable value of eyelids clearly differentiating between opened and closed eyelid in a frame of a video sequence. The proposed R\n                    <jats:sub>T<\/jats:sub>\n                    REB is classified with existing classifiers SVM, QSVM, KNN and DTREE. Increased accuracy to 99.8% validates the effectiveness and correctness of R\n                    <jats:sub>T<\/jats:sub>\n                    REB.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169811","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T18:11:28Z","timestamp":1533060688000},"page":"5279-5286","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Reckoning number of eye blinks using eye facet correlation for exigency detection"],"prefix":"10.1177","volume":"35","author":[{"given":"Puneet Singh","family":"Lamba","sequence":"first","affiliation":[{"name":"University School of Information, Communication and Technology, GGSIPU, Dwarka, Delhi, India"},{"name":"Bharati Vidyapeeth\u2019s College of Engineering, Paschim Vihar, Delhi, India"}]},{"given":"Deepali","family":"Virmani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bhagwan Parshuram Institute of Technology, Delhi, India"}]}],"member":"179","published-online":{"date-parts":[[2018,7,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"DanismanT. 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PavaniS.-K. ButakoffC. and FrangiA.F. Automatic assessment of eye blinking patterns through statistical shape models In ICVS 2009.","DOI":"10.1007\/978-3-642-04667-4_4"},{"key":"e_1_3_1_11_2","unstructured":"SoukupovaT. and CechJ. Real-Time Eye Blink Detection using Facial Landmarks 21st Computer vision Winter Workshop Cehovin Luka RokMandeljc VitomirStruc (eds.) RimskeToplice Slovenia February 3-5 2016."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2010.08.034"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.5201\/ipol.2014.104"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"e_1_3_1_16_2","unstructured":"ChauM. and BetkeM. 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