Alvin Titus R. Angus, John Alvin P. Guillen, Maurice Laurence G. Lenon, Ray Justin C. Principe, Gerald P. Feudo, Kanny Krizzy D. Serrano
{"title":"A clustering system utilizing acquired age and gender demographics thru facial detection and recognition technology","authors":"Alvin Titus R. Angus, John Alvin P. Guillen, Maurice Laurence G. Lenon, Ray Justin C. Principe, Gerald P. Feudo, Kanny Krizzy D. Serrano","doi":"10.1109/HNICEM.2017.8269504","DOIUrl":null,"url":null,"abstract":"Clustering has become relevant to an increasing amount of applications, particularly since the rise of social platforms and electronic marketing. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of facial recognition and classification. Our design for a Clustering system asses a particular group on what type of relation they have from each other, this system takes advantage of both demographic information taken from facial recognition and the interpersonal distance of the audience from one another.","PeriodicalId":104407,"journal":{"name":"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HNICEM.2017.8269504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Clustering has become relevant to an increasing amount of applications, particularly since the rise of social platforms and electronic marketing. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of facial recognition and classification. Our design for a Clustering system asses a particular group on what type of relation they have from each other, this system takes advantage of both demographic information taken from facial recognition and the interpersonal distance of the audience from one another.