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    • 3. 发明授权
    • Apparatus for and method of feature extraction for image recognition
    • 用于图像识别的特征提取的装置和方法
    • US07715659B2
    • 2010-05-11
    • US10893346
    • 2004-07-19
    • Jiali ZhaoDejun WangHaibing RenSeokcheol Kee
    • Jiali ZhaoDejun WangHaibing RenSeokcheol Kee
    • G06K9/54
    • G06K9/00248G06K9/00288
    • An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.
    • 一种用于执行其中将面部图像分成子区域的最具信息特征提取(MIFE)方法的装置和方法,并且每个子区域对进行面部识别作出个人贡献。 具体地说,为了解决不同的照明和表达,每个子区域经历基于子区域的自适应伽马(SadaGamma)校正或基于子区域的直方图均衡(SHE)。 一组参考图像也被划分成子区域并进行SadaGamma校正或SHE。 在每个校正子区域和参考图像的每个相应子区域之间进行比较。 基于针对面部图像的子区域的单独比较,选择具有最大对应关系的所存储的参考图像之一。 虽然可以单独使用,但使用MIFE和/或SadaGamma校正或SHE一起可以在不同表达,照明和遮挡下的脸部识别中实现较低的误差比。
    • 6. 发明授权
    • Image verification method, medium, and apparatus using a kernel based discriminant analysis with a local binary pattern (LBP)
    • 使用基于内核的判别分析与局部二进制模式(LBP)的图像验证方法,介质和装置,
    • US07558763B2
    • 2009-07-07
    • US11454913
    • 2006-06-19
    • Jiali ZhaoSeokcheol KeeHaitao WangHaibing RenWonjun Hwang
    • Jiali ZhaoSeokcheol KeeHaitao WangHaibing RenWonjun Hwang
    • G06F15/18
    • G06K9/00281G06K9/00275G06K2009/4666
    • A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.
    • 一种使用局部二值模式(LBP)判别技术的图像验证方法,介质和装置。 验证方法包括通过使用输入图像的LBP特征来生成内核渔夫判别分析(KFDA)基向量,通过使用预先登记的图像的LBP特征和核LBP特征来获得奇方内积,并且投影到 KFDA基矢量,通过使用查询图像的LBP特征和内核LBP特征获得奇方内积,并且投影到KFDA基向量,并且获得被获得的目标图像和查询图像的相似度 Chi Square内部产品成果,并预计以KFDA为基础。 根据该方法,介质和装置,基于KFDA的LBP显示出优于常规LBP,KFDA和生物测定实验环境(BEE)基线算法的性能。
    • 8. 发明申请
    • Image verification method, medium, and apparatus using a kernel based discriminant analysis with a local binary pattern (LBP)
    • 使用基于内核的判别分析与局部二进制模式(LBP)的图像验证方法,介质和装置,
    • US20070112699A1
    • 2007-05-17
    • US11454913
    • 2006-06-19
    • Jiali ZhaoSeokcheol KeeHaitao WangHaibing RenWonjun Hwang
    • Jiali ZhaoSeokcheol KeeHaitao WangHaibing RenWonjun Hwang
    • G06F15/18
    • G06K9/00281G06K9/00275G06K2009/4666
    • A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.
    • 一种使用局部二值模式(LBP)判别技术的图像验证方法,介质和装置。 验证方法包括通过使用输入图像的LBP特征来生成内核渔夫判别分析(KFDA)基向量,通过使用预先登记的图像的LBP特征和核LBP特征来获得奇方内积,并且投影到 KFDA基矢量,通过使用查询图像的LBP特征和内核LBP特征获得奇方内积,并且投影到KFDA基向量,并且获得被获得的目标图像和查询图像的相似度 Chi Square内部产品成果,并预计以KFDA为基础。 根据该方法,介质和装置,基于KFDA的LBP显示出优于常规LBP,KFDA和生物测定实验环境(BEE)基线算法的性能。
    • 9. 发明申请
    • Method, apparatus, and medium for removing shading of image
    • 用于去除图像阴影的方法,设备和介质
    • US20060285769A1
    • 2006-12-21
    • US11455772
    • 2006-06-20
    • Haitao WangSeokcheol KeeJiali ZhaoHaibing Ren
    • Haitao WangSeokcheol KeeJiali ZhaoHaibing Ren
    • G06K9/40
    • G06K9/00241G06T5/008G06T5/20G06T5/50G06T7/13
    • A method, apparatus, and medium for removing shading of an image are provided. The method of removing shading of an image includes: smoothing an input image; performing a gradient operation for the input image; performing normalization using the smoothed image and the images for which the gradient operation is performed; and integrating the normalized images. The apparatus for removing shading of an image includes: a smoothing unit smoothing an input image using a predetermined smoothing kernel; a gradient operation unit performing a gradient operation for the input image using a predetermined gradient operator; a normalization unit performing normalization using the smoothed image and the images for which the gradient operation is performed; and an image integration unit integrating the normalized images. According to the method, apparatus, and medium, by defining a face image model analysis and intrinsic and extrinsic factors and setting up a rational assumption, an integral normalized gradient image not sensitive to illumination is provided. Also, by employing an anisotropic diffusion method, a moire phenomenon in an edge region of an image can be avoided.
    • 提供了用于去除图像的阴影的方法,装置和介质。 去除图像的阴影的方法包括:平滑输入图像; 对所述输入图像执行梯度操作; 使用平滑化图像和执行梯度操作的图像来执行归一化; 并整合归一化图像。 用于去除图像的阴影的装置包括:平滑单元,使用预定的平滑核平滑输入图像; 梯度操作单元,使用预定的梯度算子对所述输入图像执行梯度操作; 归一化单元,其使用所述平滑图像和执行了所述梯度操作的图像来执行归一化; 以及整合归一化图像的图像积分单元。 根据该方法,装置和介质,通过定义面部图像模型分析和内在和外在因素并建立合理假设,提供了对照明不敏感的积分归一化梯度图像。 此外,通过使用各向异性扩散法,可以避免图像边缘区域中的莫尔条纹现象。
    • 10. 发明申请
    • Apparatus for and method of feature extraction for image recognition
    • 用于图像识别的特征提取的装置和方法
    • US20060008150A1
    • 2006-01-12
    • US10893346
    • 2004-07-19
    • Jiali ZhaoDejun WangHaibing RenSeokcheol Kee
    • Jiali ZhaoDejun WangHaibing RenSeokcheol Kee
    • G06K9/46G06K9/00
    • G06K9/00248G06K9/00288
    • An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.
    • 一种用于执行其中将面部图像分成子区域的最具信息特征提取(MIFE)方法的装置和方法,并且每个子区域对进行面部识别作出个人贡献。 具体地说,为了解决不同的照明和表达,每个子区域经受基于子区域的自适应伽马(SadaGamma)校正或基于子区域的直方图均衡(SHE)。 一组参考图像也被划分成子区域并进行SadaGamma校正或SHE。 在每个校正子区域和参考图像的每个相应子区域之间进行比较。 基于针对面部图像的子区域的单独比较,选择具有最大对应关系的所存储的参考图像之一。 虽然可以单独使用,但使用MIFE和/或SadaGamma校正或SHE一起可以在不同表达,照明和遮挡下的脸部识别中实现较低的误差比。