Onfocus detection:identifying individual-camera eye contact from unconstrained images  

作　　者：Dingwen ZHANG Bo WANG Gerong WANG Qiang ZHANG Jiajia ZHANG Jungong HAN Zheng YOU 

机构地区：[1]School of Mechano-Electronic Engineering,Xidian University,Xi'an 710071,China [2]State Key Laboratory of Precision Measurement Technology and Instruments,Tsinghua University,Beijing 100084,China [3]Computer Science Department,Aberystwyth University,Ceredigion SY233FL,UK [4]Beijing Jingzhen Medical Technology Ltd.,Beijing 100084,China

出　　处：《Science China(Information Sciences)》2022年第6期1-12,共12页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.61876140,61773301);Fundamental Research Funds for the Central Universities(Grant No.JBZ170401);China Postdoctoral Support Scheme for Innovative Talents(Grant No.BX20180236)。

摘　　要：Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not.Based on the behavioral research,the focus of an individual during face-to-camera communication leads to a special type of eye contact,i.e.,the individual-camera eye contact,which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status(e.g.,lying or suffering mental disease)and special purposes(e.g.,seeking help or attracting fans).Thus,developing effective onfocus detection algorithms is of significance for assisting the criminal investigation,disease discovery,and social behavior analysis.However,the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task.To this end,this paper engages in the onfocus detection research by addressing the above two issues.Firstly,we build a large-scale onfocus detection dataset,named as the onfocus detection in the wild(OFDIW).It consists of 20623 images in unconstrained capture conditions(thus called“in the wild”)and contains individuals with diverse emotions,ages,facial characteristics,and rich interactions with surrounding objects and background scenes.On top of that,we propose a novel end-to-end deep model,i.e.,the eye-context interaction inferring network(ECIIN),for onfocus detection,which explores eye-context interaction via dynamic capsule routing.Finally,comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

关 键 词：onfocus detection deep neural network capsule routing computer vision deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]