发明授权
US07379568B2 Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus
有权
弱假设产生装置和方法,学习装置和方法,检测装置和方法,面部表情学习装置和方法,面部表情识别装置和方法以及机器人装置
- 专利标题: Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus
- 专利标题(中): 弱假设产生装置和方法,学习装置和方法,检测装置和方法,面部表情学习装置和方法,面部表情识别装置和方法以及机器人装置
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申请号: US10871494申请日: 2004-06-17
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公开(公告)号: US07379568B2公开(公告)日: 2008-05-27
- 发明人: Javier R. Movellan , Marian S. Bartlett , Gwendolen C. Littlewort , John Hershey , Ian R. Fasel , Eric C. Carlson , Josh Susskind , Kohtaro Sabe , Kenta Kawamoto , Kenichi Hidai
- 申请人: Javier R. Movellan , Marian S. Bartlett , Gwendolen C. Littlewort , John Hershey , Ian R. Fasel , Eric C. Carlson , Josh Susskind , Kohtaro Sabe , Kenta Kawamoto , Kenichi Hidai
- 申请人地址: JP Tokyo US CA Oakland
- 专利权人: Sony Corporation,San Diego, University of California
- 当前专利权人: Sony Corporation,San Diego, University of California
- 当前专利权人地址: JP Tokyo US CA Oakland
- 代理机构: Frommer Lawrence & Haug LLP
- 代理商 William S. Frommer
- 优先权: JP2003-417191 20031215
- 主分类号: G06K9/00
- IPC分类号: G06K9/00
摘要:
A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.
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