标题:Appearance-Based gaze block estimation via CNN classification
作者:Wu, Xuemei ;Li, Jing ;Wu, Qiang ;Sun, Jiande
作者机构:[Wu, Xuemei ;Wu, Qiang ;Sun, Jiande ] School of Information Science and Engineering, Shandong University, Jinan, China;[Li, Jing ] School of Mechanica 更多
会议名称:19th IEEE International Workshop on Multimedia Signal Processing, MMSP 2017
会议日期:16 October 2017 through 18 October 2017
来源:2017 IEEE 19th International Workshop on Multimedia Signal Processing, MMSP 2017
出版年:2017
卷:2017-January
页码:1-5
DOI:10.1109/MMSP.2017.8122270
关键词:Appearance-Based; Button-Touch-Based Interaction; CNN; Gaze block; Gaze Estimation
摘要:Appearance-based gaze estimation methods have received increasing attention in the field of human-computer interaction (HCI). These methods tried to estimate the accurate gaze point via Convolutional Neural Network (CNN) model, but the estimated accuracy can't reach the requirement of gazebased HCI when the regression model is used in the output layer of CNN. Given the popularity of button-touch-based interaction, we propose an appearance-based gaze block estimation method, which aims to estimate the gaze block, not the gaze point. In the proposed method, we relax the estimation from point to block, so that the gaze block can be estimated by CNN-based classification instead of the previous regression model. We divide the screen into square blocks to imitate the button-touch interface, and build an eye-image dataset, which contains the eye images labelled by their corresponding gaze blocks on the screen. We train the CNN model according to this dataset to estimate the gaze block by classifying the eye images. The experiments on 6- A nd 54-block classifications demonstrate that the proposed method has high accuracy in gaze block estimation without any calibration, and it is promising in button-touch-based interaction. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043397053&doi=10.1109%2fMMSP.2017.8122270&partnerID=40&md5=fde3a862d69f1bb55d8e4e9b167d6e3a
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