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":"{\"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}","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}
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.