{"title":"Computer Model for Disguishing Asian People by Face","authors":"B. Erdene, T. Ganbat","doi":"10.1109/U-MEDIA.2014.26","DOIUrl":null,"url":null,"abstract":"In this paper we aim to create new effective computer model for distinguishing Asian people by their face using frontal face part color, size and distances based on comparative research about image processing and face recognition. It is difficult to determine face part size and distance due to image quality, lighting condition, rotation angle and facial emotion. Hence, first we need to detect face on the image then convert image into real input. After that we can determine image candidate's gender, face shape, key points and face parts. Finally, we will return the result, based on comparison of sizes and distances with the sample's measurement table database. While we were measuring samples, there were big differences between images by their gender and face shapes. Input images must be the frontal face image that has smooth lighting and does not have any rotation angel. The model can be used in military, police, defense, health care, and technology sectors.","PeriodicalId":174849,"journal":{"name":"2014 7th International Conference on Ubi-Media Computing and Workshops","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 7th International Conference on Ubi-Media Computing and Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/U-MEDIA.2014.26","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
In this paper we aim to create new effective computer model for distinguishing Asian people by their face using frontal face part color, size and distances based on comparative research about image processing and face recognition. It is difficult to determine face part size and distance due to image quality, lighting condition, rotation angle and facial emotion. Hence, first we need to detect face on the image then convert image into real input. After that we can determine image candidate's gender, face shape, key points and face parts. Finally, we will return the result, based on comparison of sizes and distances with the sample's measurement table database. While we were measuring samples, there were big differences between images by their gender and face shapes. Input images must be the frontal face image that has smooth lighting and does not have any rotation angel. The model can be used in military, police, defense, health care, and technology sectors.