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公开(公告)号：US20160275721A1
公开(公告)日：2016-09-22
申请号：US14443337
申请日：2014-06-20
申请人： Minje PARK , Olivier DUCHENNE , Yeongjae CHEON , Tae- Hoon KIM , Xiaolu SHEN , Yangzhou DU , Wooju RYU , Myung-He JU , INTEL CORPORATION
发明人： Minje PARK , Olivier DUCHENNE , Yeongjae CHEON , Tae-Hoon KIM , Xiaolu SHEN , Yangzhou DU , Wooju RYU , Myung-Ho JU
CPC分类号： G06T17/205 , G06K9/00208 , G06K9/00214 , G06K9/00221 , G06K9/00248 , G06K9/00261 , G06K9/00281 , G06T7/251 , G06T17/20 , G06T2200/04 , G06T2207/30201
摘要： Apparatuses, methods and storage medium associated with 3D face model reconstruction are disclosed herein. In embodiments, an apparatus may include a facial landmark detector, a model fitter and a model tracker. The facial landmark detector may be configured to detect a plurality of landmarks of a face and their locations within each of a plurality of image frames. The model fitter may be configured to generate a 3D model of the face from a 3D model of a neutral face, in view of detected landmarks of the face and their locations within a first one of the plurality of image frames. The model tracker may be configured to maintain the 3D model to track the face in subsequent image frames, successively updating the 3D model in view of detected landmarks of the face and their locations within each of successive ones of the plurality of image frames. In embodiments, the facial landmark detector may include a face detector, an initial facial landmark detector, and one or more facial landmark detection linear regressors. Other embodiments may be described and/or claimed.
摘要翻译： 本文公开了与3D脸部模型重建相关联的装置，方法和存储介质。 在实施例中，装置可以包括面部地标检测器，模型装配器和模型跟踪器。 面部地标检测器可以被配置为检测面部的多个界标及其在多个图像帧的每一个内的位置。 考虑到检测到的面部的标记及其在多个图像帧的第一个图像帧中的位置，模型拟合器可以被配置为从中立面的3D模型生成面部的3D模型。 模型跟踪器可以被配置为维持3D模型以跟踪后续图像帧中的面部，从而考虑到检测到的面部的界标及其在多个图像帧中的每个连续图像帧中的位置之间的连续更新3D模型。 在实施例中，面部地标检测器可以包括面部检测器，初始面部地标检测器和一个或多个面部地标检测线性回归器。 可以描述和/或要求保护其他实施例。
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2.发明申请Method, Apparatus and Computer Readable Recording Medium for Detecting a Location of a Face Feature Point Using an Adaboost Learning Algorithm 审中-公开使用Adaboost学习算法检测脸部特征点的位置的方法，装置和计算机可读记录介质公开(公告)号：US20160078319A1
公开(公告)日：2016-03-17
申请号：US14926284
申请日：2015-10-29
申请人： Yeongjae Cheon , Yongchan PARK
发明人： Yeongjae Cheon , Yongchan PARK
CPC分类号： G06K9/6257 , G06K9/00221 , G06K9/00228 , G06K9/00281 , G06K9/00288 , G06K9/3233 , G06K9/6202 , G06K9/6219 , G06K9/6221 , G06K9/6227 , G06K9/6232 , G06K9/6262
摘要： The present disclosure relates to detecting the location of a face feature point using an Adaboost learning algorithm. According to some embodiments, a method for detecting a location of a face feature point comprises: (a) a step of classifying a sub-window image into a first recommended feature point candidate image and a first non-recommended feature point candidate image using first feature patterns selected by an Adaboost learning algorithm, and generating first feature point candidate location information on the first recommended feature point candidate image; and (b) a step of re-classifying said sub-window image classified into said first non-recommended feature point candidate image, into a second recommended feature point candidate image and a second non-recommended feature point candidate image using second feature patterns selected by the Adaboost learning algorithm, and generating second feature point candidate location information on the second recommended feature point recommended candidate image.
摘要翻译： 本公开涉及使用Adaboost学习算法来检测脸部特征点的位置。 根据一些实施例，一种用于检测面部特征点的位置的方法包括：（a）使用第一方法将子窗口图像分类为第一推荐特征点候选图像和第一非推荐特征点候选图像的步骤 通过Adaboost学习算法选择的特征图案，以及在第一推荐特征点候选图像上生成第一特征点候选位置信息; 和（b）使用所选择的第二特征图案将分类为所述第一非推荐特征点候选图像的所述子窗口图像重新分类为第二推荐特征点候选图像和第二非推荐特征点候选图像的步骤 通过Adaboost学习算法，并且在第二推荐特征点推荐候选图像上生成第二特征点候选位置信息。
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公开(公告)号：US09679412B2
公开(公告)日：2017-06-13
申请号：US14443337
申请日：2014-06-20
申请人： INTEL CORPORATION , Minje Park , Olivier Duchenne , Yeongjae Cheon , Tae-Hoon Kim , Xiaolu Shen , Yangzhou Du , Wooju Ryu , Myung-Ho Ju
发明人： Minje Park , Olivier Duchenne , Yeongjae Cheon , Tae-Hoon Kim , Xiaolu Shen , Yangzhou Du , Wooju Ryu , Myung-Ho Ju
CPC分类号： G06T17/205 , G06K9/00208 , G06K9/00214 , G06K9/00221 , G06K9/00248 , G06K9/00261 , G06K9/00281 , G06T7/251 , G06T17/20 , G06T2200/04 , G06T2207/30201
摘要： Apparatuses, methods and storage medium associated with 3D face model reconstruction are disclosed herein. In embodiments, an apparatus may include a facial landmark detector, a model fitter and a model tracker. The facial landmark detector may be configured to detect a plurality of landmarks of a face and their locations within each of a plurality of image frames. The model fitter may be configured to generate a 3D model of the face from a 3D model of a neutral face, in view of detected landmarks of the face and their locations within a first one of the plurality of image frames. The model tracker may be configured to maintain the 3D model to track the face in subsequent image frames, successively updating the 3D model in view of detected landmarks of the face and their locations within each of successive ones of the plurality of image frames. In embodiments, the facial landmark detector may include a face detector, an initial facial landmark detector, and one or more facial landmark detection linear regressors. Other embodiments may be described and/or claimed.
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4.发明授权Method, apparatus and computer readable recording medium for detecting a location of a face feature point using an Adaboost learning algorithm 有权用于使用Adaboost学习算法检测脸部特征点的位置的方法，装置和计算机可读记录介质公开(公告)号：US09202109B2
公开(公告)日：2015-12-01
申请号：US14129356
申请日：2012-09-27
申请人： Yeongjae Cheon , Yongchan Park
发明人： Yeongjae Cheon , Yongchan Park
CPC分类号： G06K9/6257 , G06K9/00221 , G06K9/00228 , G06K9/00281 , G06K9/00288 , G06K9/3233 , G06K9/6202 , G06K9/6219 , G06K9/6221 , G06K9/6227 , G06K9/6232 , G06K9/6262
摘要： The present disclosure relates to detecting the location of a face feature point using an Adaboost learning algorithm. According to some embodiments, a method for detecting a location of a face feature point comprises: (a) a step of classifying a sub-window image into a first recommended feature point candidate image and a first non-recommended feature point candidate image using first feature patterns selected by an Adaboost learning algorithm, and generating first feature point candidate location information on the first recommended feature point candidate image; and (b) a step of re-classifying said sub-window image classified into said first non-recommended feature point candidate image, into a second recommended feature point candidate image and a second non-recommended feature point candidate image using second feature patterns selected by the Adaboost learning algorithm, and generating second feature point candidate location information on the second recommended feature point recommended candidate image.
摘要翻译： 本公开涉及使用Adaboost学习算法来检测脸部特征点的位置。 根据一些实施例，一种用于检测面部特征点的位置的方法包括：（a）使用第一方法将子窗口图像分类为第一推荐特征点候选图像和第一非推荐特征点候选图像的步骤 通过Adaboost学习算法选择的特征图案，以及在第一推荐特征点候选图像上生成第一特征点候选位置信息; 和（b）使用所选择的第二特征图案将分类为所述第一非推荐特征点候选图像的所述子窗口图像重新分类为第二推荐特征点候选图像和第二非推荐特征点候选图像的步骤 通过Adaboost学习算法，并且在第二推荐特征点推荐候选图像上生成第二特征点候选位置信息。
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