Insu Kim, Keunwoo Park, Youngwoo Yoon, Geehyuk Lee
{"title":"Touch180","authors":"Insu Kim, Keunwoo Park, Youngwoo Yoon, Geehyuk Lee","doi":"10.1145/3266037.3266091","DOIUrl":null,"url":null,"abstract":"We present Touch180, a computer vision based solution for identifying fingers on a mobile touchscreen with a fisheye camera and deep learning algorithm. As a proof-of-concept research, this paper focused on robustness and high accuracy of finger identification. We generated a new dataset for Touch180 configuration, which is named as Fisheye180. We trained a CNN (Convolutional Neural Network)-based network utilizing touch locations as auxiliary inputs. With our novel dataset and deep learning algorithm, finger identification result shows 98.56% accuracy with VGG16 model. Our study will serve as a step stone for finger identification on a mobile touchscreen.","PeriodicalId":421706,"journal":{"name":"The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3266037.3266091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We present Touch180, a computer vision based solution for identifying fingers on a mobile touchscreen with a fisheye camera and deep learning algorithm. As a proof-of-concept research, this paper focused on robustness and high accuracy of finger identification. We generated a new dataset for Touch180 configuration, which is named as Fisheye180. We trained a CNN (Convolutional Neural Network)-based network utilizing touch locations as auxiliary inputs. With our novel dataset and deep learning algorithm, finger identification result shows 98.56% accuracy with VGG16 model. Our study will serve as a step stone for finger identification on a mobile touchscreen.