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公开(公告)号：US20160132718A1
公开(公告)日：2016-05-12
申请号：US14535133
申请日：2014-11-06
Applicant: Intel Corporation
Inventor： Minje Park , Hyungsoo Lee , Hongmo Je , Tae-Hoon Kim , Yeongjae Cheon , Taesup Kim
CPC classification number: G06K9/3275 , G06K9/00248
Abstract: Computer-readable storage media, computing devices and methods are discussed herein. In embodiments, a computing device may be configured to perform facial recognition based on gradient based feature extractions of images of faces. In embodiments, the computing device may be configured to determine directional matching patterns of the images from the gradient based feature extraction and may utilize these directional matching patterns in performing a facial recognition analysis of the images of faces. Other embodiments may be described and/or claimed.
Abstract translation: 本文讨论了计算机可读存储介质，计算设备和方法。 在实施例中，计算设备可以被配置为基于面部图像的基于梯度的特征提取来执行面部识别。 在实施例中，计算设备可以被配置为从基于梯度的特征提取来确定图像的方向匹配模式，并且可以在对面部图像执行面部识别分析时利用这些方向匹配模式。 可以描述和/或要求保护其他实施例。
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2.发明申请公开(公告)号：US20180165551A1
公开(公告)日：2018-06-14
申请号：US15372953
申请日：2016-12-08
Applicant: Intel Corporation
Inventor： Byungseok Roh , Kye-Hyeon Kim , Sanghoon Hong , Minje Park , Yeongjae Cheon
CPC classification number: G06K9/3233 , G06K9/4628 , G06N3/0454
Abstract: Technologies for multi-scale object detection include a computing device including a multi-layer convolution network and a multi-scale region proposal network (RPN). The multi-layer convolution network generates a convolution map based on an input image. The multi-scale RPN includes multiple RPN layers, each with a different receptive field size. Each RPN layer generates region proposals based on the convolution map. The computing device may include a multi-scale object classifier that includes multiple region of interest (ROI) pooling layers and multiple associated fully connected (FC) layers. Each ROI pooling layer has a different output size, and each FC layer may be trained for an object scale based on the output size of the associated ROI pooling layer. Each ROI pooling layer may generate pooled ROIs based on the region proposals and each FC layer may generate object classification vectors based on the pooled ROIs. Other embodiments are described and claimed.
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3.发明申请FACILITATING EFFICEINT FREE IN-PLANE ROTATION LANDMARK TRACKING OF IMAGES ON COMPUTING DEVICES 审中-公开在计算机设备上实现图像的免费平面图旋转朗读跟踪公开(公告)号：US20160300099A1
公开(公告)日：2016-10-13
申请号：US14762687
申请日：2014-09-25
Applicant: INTEL CORPORATION
Inventor： SHEN XIAOLU , Yangzhou Du , Minje Park , Yeongjae Cheon , Olivier Duchenne , Tae-Hoon Kim
Abstract: A mechanism is described for facilitating efficient free in-plane rotation landmark tracking of images on computing devices according to one embodiment. A method of embodiments, as described herein, includes detecting a first frame having a first image and a second frame having a second image, where the second image is rotated to a position away from the first image. The method may further include assigning a first parameter line and a second parameter line to the second image based on landmark positions associated with the first and second images, detecting a rotation angle between the first parameter line and the second parameter line, and rotating the second image back and forth within a distance associated with the rotation angle to verify positions of the first and second images.
Abstract translation: 描述了根据一个实施例的用于促进计算设备上的图像的有效的自由面内旋转地标跟踪的机构。 如本文所述的实施例的方法包括检测具有第一图像的第一帧和具有第二图像的第二帧，其中第二图像旋转到远离第一图像的位置。 该方法还可以包括基于与第一和第二图像相关联的界标位置向第二图像分配第一参数线和第二参数线，检测第一参数线和第二参数线之间的旋转角度，以及旋转第二参数线 在与旋转角度相关联的距离内来回显示图像，以验证第一和第二图像的位置。
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公开(公告)号：US09384385B2
公开(公告)日：2016-07-05
申请号：US14535133
申请日：2014-11-06
Applicant: Intel Corporation
Inventor： Minje Park , Hyungsoo Lee , Hongmo Je , Tae-Hoon Kim , Yeongjae Cheon , Taesup Kim
CPC classification number: G06K9/3275 , G06K9/00248
Abstract: Computer-readable storage media, computing devices and methods are discussed herein. In embodiments, a computing device may be configured to perform facial recognition based on gradient based feature extractions of images of faces. In embodiments, the computing device may be configured to determine directional matching patterns of the images from the gradient based feature extraction and may utilize these directional matching patterns in performing a facial recognition analysis of the images of faces. Other embodiments may be described and/or claimed.
Abstract translation: 本文讨论了计算机可读存储介质，计算设备和方法。 在实施例中，计算设备可以被配置为基于面部图像的基于梯度的特征提取来执行面部识别。 在实施例中，计算设备可以被配置为从基于梯度的特征提取来确定图像的方向匹配模式，并且可以在对面部图像执行面部识别分析时利用这些方向匹配模式。 可以描述和/或要求保护其他实施例。
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Patent Agency Ranking
公开(公告)号：US09977950B2
公开(公告)日：2018-05-22
申请号：US15008186
申请日：2016-01-27
Applicant: INTEL CORPORATION
Inventor： Hyungsoo Lee , Yeongjae Cheon , Sumin Lee , Minje Park
CPC classification number: G06K9/00288 , G06F17/30256 , G06K9/00281 , G06K9/6215
Abstract: Techniques are provided for facial recognition using decoy-based matching of facial image features. An example method may include comparing extracted facial features of an input image, provided for recognition, to facial features of each of one or more images in a gallery of known faces, to select a closest gallery image. The method may also include calculating a first distance between the input image and the selected gallery image. The method may further include comparing the facial features of the input image to facial features of each of one or more images in a set of decoy faces, to select a closest decoy image and calculating a second distance between the input image and the selected decoy image. The method may further include recognizing a match between the input image and the selected gallery image based on a comparison of the first distance and the second distance.
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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
Applicant: INTEL CORPORATION , Minje Park , Olivier Duchenne , Yeongjae Cheon , Tae-Hoon Kim , Xiaolu Shen , Yangzhou Du , Wooju Ryu , Myung-Ho Ju
Inventor： Minje Park , Olivier Duchenne , Yeongjae Cheon , Tae-Hoon Kim , Xiaolu Shen , Yangzhou Du , Wooju Ryu , Myung-Ho Ju
CPC classification number: G06T17/205 , G06K9/00208 , G06K9/00214 , G06K9/00221 , G06K9/00248 , G06K9/00261 , G06K9/00281 , G06T7/251 , G06T17/20 , G06T2200/04 , G06T2207/30201
Abstract: 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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7.发明授权公开(公告)号：US10262237B2
公开(公告)日：2019-04-16
申请号：US15372953
申请日：2016-12-08
Applicant: Intel Corporation
Inventor： Byungseok Roh , Kye-Hyeon Kim , Sanghoon Hong , Minje Park , Yeongjae Cheon
Abstract: Technologies for multi-scale object detection include a computing device including a multi-layer convolution network and a multi-scale region proposal network (RPN). The multi-layer convolution network generates a convolution map based on an input image. The multi-scale RPN includes multiple RPN layers, each with a different receptive field size. Each RPN layer generates region proposals based on the convolution map. The computing device may include a multi-scale object classifier that includes multiple region of interest (ROI) pooling layers and multiple associated fully connected (FC) layers. Each ROI pooling layer has a different output size, and each FC layer may be trained for an object scale based on the output size of the associated ROI pooling layer. Each ROI pooling layer may generate pooled ROIs based on the region proposals and each FC layer may generate object classification vectors based on the pooled ROIs. Other embodiments are described and claimed.
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公开(公告)号：US20170213074A1
公开(公告)日：2017-07-27
申请号：US15008186
申请日：2016-01-27
Applicant: INTEL CORPORATION
Inventor： Hyungsoo Lee , Yeongjae Cheon , Sumin Lee , Minje Park
CPC classification number: G06K9/00288 , G06F17/30256 , G06K9/00281 , G06K9/6215
Abstract: Techniques are provided for facial recognition using decoy-based matching of facial image features. An example method may include comparing extracted facial features of an input image, provided for recognition, to facial features of each of one or more images in a gallery of known faces, to select a closest gallery image. The method may also include calculating a first distance between the input image and the selected gallery image. The method may further include comparing the facial features of the input image to facial features of each of one or more images in a set of decoy faces, to select a closest decoy image and calculating a second distance between the input image and the selected decoy image. The method may further include recognizing a match between the input image and the selected gallery image based on a comparison of the first distance and the second distance.
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9.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：US09563821B2
公开(公告)日：2017-02-07
申请号：US14926284
申请日：2015-10-29
Applicant: Intel Corporation
Inventor： Yeongjae Cheon , Yongchan Park
CPC classification number: G06K9/6257 , G06K9/00221 , G06K9/00228 , G06K9/00281 , G06K9/00288 , G06K9/3233 , G06K9/6202 , G06K9/6219 , G06K9/6221 , G06K9/6227 , G06K9/6232 , G06K9/6262
Abstract: 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.
Abstract translation: 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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