发明名称 FACIAL EXPRESSION TRAINING USING FEEDBACK FROM AUTOMATIC FACIAL EXPRESSION RECOGNITION
摘要 A machine learning classifier is trained to compute a quality measure of a facial expression with respect to a predetermined emotion, affective state, or situation. The expression may be of a person or an animated character. The quality measure may be provided to a person. The quality measure may also used to tune the appearance parameters of the animated character, including texture parameters. People may be trained to improve their expressiveness based on the feedback of the quality measure provided by the machine learning classifier, for example, to improve the quality of customer interactions, and to mitigate the symptoms of various affective and neurological disorders. The classifier may be built into a variety of mobile devices, including wearable devices such as Google Glass and smart watches.
申请公布号 US2014242560(A1) 申请公布日期 2014.08.28
申请号 US201414182286 申请日期 2014.02.17
申请人 Emotient 发明人 MOVELLAN Javier;BARTLETT Marian Steward;FASEL Ian;LITTLEWORT Gwen Ford;SUSSKIND Joshua;Denman Ken;WHITEHILL Jacob
分类号 G09B19/00 主分类号 G09B19/00
代理机构 代理人
主权项 1. A computer-implemented method comprising steps of: capturing data representing facial expression appearance of a user; analyzing the data representing the facial expression appearance of the user with a machine learning classifier to obtain a quality measure estimate of the facial expression appearance with respect to a predetermined prompt; and providing to the user the quality measure estimate.
地址 San Diego CA US