{"title":"移动设备上基于深度特征的人脸检测","authors":"Sayantan Sarkar, Vishal M. Patel, R. Chellappa","doi":"10.1109/ISBA.2016.7477230","DOIUrl":null,"url":null,"abstract":"We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front-facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameworks, to meet these challenges.","PeriodicalId":198009,"journal":{"name":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Deep feature-based face detection on mobile devices\",\"authors\":\"Sayantan Sarkar, Vishal M. Patel, R. Chellappa\",\"doi\":\"10.1109/ISBA.2016.7477230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front-facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameworks, to meet these challenges.\",\"PeriodicalId\":198009,\"journal\":{\"name\":\"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBA.2016.7477230\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBA.2016.7477230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep feature-based face detection on mobile devices
We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front-facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the special nature of the images captured by the front-facing camera of mobile devices and exploits the GPUs present in mobile devices without CUDA-based frameworks, to meet these challenges.