Deep learning fixation prediction method and system based on face appearance
The invention discloses a deep learning fixation prediction method and system based on face appearance. The method comprises the following steps: acquiring a face appearance picture of a to-be-detected object; processing the face appearance picture of the to-be-detected object, and extracting a face...
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creator | CHEN JIAHUI WANG XIWEN LI YUJIE MA JIAXIN HUANG LONGZHAO DING SHUXUE |
description | The invention discloses a deep learning fixation prediction method and system based on face appearance. The method comprises the following steps: acquiring a face appearance picture of a to-be-detected object; processing the face appearance picture of the to-be-detected object, and extracting a face picture; and inputting the face picture into a trained fixation prediction model, and predicting a human eye fixation direction in the face picture. According to the method, the convolutional neural network, the window multi-head attention mechanism and the moving window multi-head attention mechanism are combined, so that the image local spatial feature learning ability and the global feature modeling ability of the network are improved; the problems that an existing gaze prediction method based on a visual transformer cannot achieve multi-scale feature learning and image global self-attention calculation is difficult are solved.
本发明公开了一种基于人脸外观的深度学习注视预测方法及系统,方法包括:获取待测对象的人脸外观图片;对所述待测对象的人脸外观图片进行处理,提取人脸图片;将所述人脸图片输入训 |
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本发明公开了一种基于人脸外观的深度学习注视预测方法及系统,方法包括:获取待测对象的人脸外观图片;对所述待测对象的人脸外观图片进行处理,提取人脸图片;将所述人脸图片输入训</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230728&DB=EPODOC&CC=CN&NR=116503937A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230728&DB=EPODOC&CC=CN&NR=116503937A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHEN JIAHUI</creatorcontrib><creatorcontrib>WANG XIWEN</creatorcontrib><creatorcontrib>LI YUJIE</creatorcontrib><creatorcontrib>MA JIAXIN</creatorcontrib><creatorcontrib>HUANG LONGZHAO</creatorcontrib><creatorcontrib>DING SHUXUE</creatorcontrib><title>Deep learning fixation prediction method and system based on face appearance</title><description>The invention discloses a deep learning fixation prediction method and system based on face appearance. The method comprises the following steps: acquiring a face appearance picture of a to-be-detected object; processing the face appearance picture of the to-be-detected object, and extracting a face picture; and inputting the face picture into a trained fixation prediction model, and predicting a human eye fixation direction in the face picture. According to the method, the convolutional neural network, the window multi-head attention mechanism and the moving window multi-head attention mechanism are combined, so that the image local spatial feature learning ability and the global feature modeling ability of the network are improved; the problems that an existing gaze prediction method based on a visual transformer cannot achieve multi-scale feature learning and image global self-attention calculation is difficult are solved.
本发明公开了一种基于人脸外观的深度学习注视预测方法及系统,方法包括:获取待测对象的人脸外观图片;对所述待测对象的人脸外观图片进行处理,提取人脸图片;将所述人脸图片输入训</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPBxSU0tUMhJTSzKy8xLV0jLrEgsyczPUygoSk3JTAYzc1NLMvJTFBLzUhSKK4tLUnMVkhKLU1MUgFJpicmpCokFBUDtiXnJqTwMrGmJOcWpvFCam0HRzTXE2UM3tSA_PrW4AKg6L7Uk3tnP0NDM1MDY0tjc0ZgYNQCPqDWu</recordid><startdate>20230728</startdate><enddate>20230728</enddate><creator>CHEN JIAHUI</creator><creator>WANG XIWEN</creator><creator>LI YUJIE</creator><creator>MA JIAXIN</creator><creator>HUANG LONGZHAO</creator><creator>DING SHUXUE</creator><scope>EVB</scope></search><sort><creationdate>20230728</creationdate><title>Deep learning fixation prediction method and system based on face appearance</title><author>CHEN JIAHUI ; WANG XIWEN ; LI YUJIE ; MA JIAXIN ; HUANG LONGZHAO ; DING SHUXUE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116503937A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>CHEN JIAHUI</creatorcontrib><creatorcontrib>WANG XIWEN</creatorcontrib><creatorcontrib>LI YUJIE</creatorcontrib><creatorcontrib>MA JIAXIN</creatorcontrib><creatorcontrib>HUANG LONGZHAO</creatorcontrib><creatorcontrib>DING SHUXUE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHEN JIAHUI</au><au>WANG XIWEN</au><au>LI YUJIE</au><au>MA JIAXIN</au><au>HUANG LONGZHAO</au><au>DING SHUXUE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Deep learning fixation prediction method and system based on face appearance</title><date>2023-07-28</date><risdate>2023</risdate><abstract>The invention discloses a deep learning fixation prediction method and system based on face appearance. The method comprises the following steps: acquiring a face appearance picture of a to-be-detected object; processing the face appearance picture of the to-be-detected object, and extracting a face picture; and inputting the face picture into a trained fixation prediction model, and predicting a human eye fixation direction in the face picture. According to the method, the convolutional neural network, the window multi-head attention mechanism and the moving window multi-head attention mechanism are combined, so that the image local spatial feature learning ability and the global feature modeling ability of the network are improved; the problems that an existing gaze prediction method based on a visual transformer cannot achieve multi-scale feature learning and image global self-attention calculation is difficult are solved.
本发明公开了一种基于人脸外观的深度学习注视预测方法及系统,方法包括:获取待测对象的人脸外观图片;对所述待测对象的人脸外观图片进行处理,提取人脸图片;将所述人脸图片输入训</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | Deep learning fixation prediction method and system based on face appearance |
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