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    • 6. 发明申请
    • Face Feature Vector Construction
    • 面部特征矢量构造
    • US20130155063A1
    • 2013-06-20
    • US13332084
    • 2011-12-20
    • Jan Erik SolemMichael Rousson
    • Jan Erik SolemMichael Rousson
    • G06T17/00
    • G06K9/00221G06K9/00275G06K9/621
    • Systems, methods, and computer readable media for determining and applying face recognition parameter sets are described. In general, techniques are disclosed for identifying and constructing a unique combination of facial recognition discriminators into a “face feature vector” that has been found to be more robust (e.g., stable to image noise, a person's pose, and scene illumination) and accurate (e.g., provide high recognition rates) than prior art techniques. More particularly, a face feature vector may be generated by the combination of shape descriptors (e.g., as generated by two-dimensional and three-dimensional shape models) and texture descriptors (e.g., as generated by global and local texture models).
    • 描述了用于确定和应用面部识别参数集的系统,方法和计算机可读介质。 通常,公开了用于将面部识别鉴别器的唯一组合识别和构造成已被发现更加鲁棒的“面部特征向量”(例如,稳定于图像噪声,人的姿势和场景照明)并且准确的技术 (例如,提供高识别率)比现有技术。 更具体地,可以通过形状描述符(例如,由二维和三维形状模型生成)和纹理描述符(例如,由全局和局部纹理模型生成)的组合来生成面部特征向量。
    • 7. 发明授权
    • Method of computing global-to-local metrics for recognition
    • 计算用于识别的全局到本地度量的方法
    • US08488873B2
    • 2013-07-16
    • US12574717
    • 2009-10-07
    • Mikael RoussonJan Erik SolemJerome Piovano
    • Mikael RoussonJan Erik SolemJerome Piovano
    • G06K9/62G06K9/00
    • G06K9/6215G06N99/005
    • A method of computing global-to-local metrics for recognition. Based on training examples with feature representations, the method automatically computes a local metric that varies over the space of feature representations to optimize discrimination and the performance of recognition systems.Given a set of points in an arbitrary features space, local metrics are learned in a hierarchical manner that give low distances between points of same class and high distances between points of different classes. Rather than considering a global metric, a class-based metric or a point-based metric, the proposed invention applies successive clustering to the data and associates a metric to each one of the clusters.
    • 计算用于识别的全局到本地度量的方法。 基于具有特征表示的训练示例,该方法自动计算在特征表示空间上变化的局部度量,以优化识别系统的识别和性能。 给定任意特征空间中的一组点,以分级方式学习局部度量,这样可以在不同类别的点之间提供相同类别的点和高距离之间的较低距离。 所提出的发明不是考虑全局度量,基于类的度量或基于点的度量,而是将连续的聚类应用于数据并将度量与每个集群相关联。
    • 8. 发明申请
    • Combining Multiple Image Detectors
    • 组合多个图像检测器
    • US20140050404A1
    • 2014-02-20
    • US13588639
    • 2012-08-17
    • Jan Erik SolemOualid MerzougaMichael Rousson
    • Jan Erik SolemOualid MerzougaMichael Rousson
    • G06K9/46
    • G06K9/00281
    • A technique for combining multiple individual feature detectors to identify a combined feature in a digital image is disclosed. A combined feature detection rule may specify multiple individual feature detectors with which an image is to be analyzed. The multiple individual feature detectors may identify constituent parts of the combined feature and/or may identify features based on different image properties. An analysis of the image with the specified feature detectors may result in the identification of multiple candidate regions (i.e., regions within which the detectors identify their respective features). The combined feature detection rule may operate directly on the multiple candidate regions to adjust the spatial properties of the candidate regions and group the adjusted candidate regions into candidate region groups, it may then be determined if one or more of the candidate region groups is representative of a presence of the combined feature in the image.
    • 公开了一种用于组合多个单独特征检测器以识别数字图像中的组合特征的技术。 组合特征检测规则可以指定要分析图像的多个单独特征检测器。 多个单独的特征检测器可以识别组合特征的组成部分和/或可以基于不同的图像属性识别特征。 使用指定的特征检测器对图像的分析可以导致多个候选区域(即,检测器识别其各自特征的区域)的识别。 组合特征检测规则可以直接在多个候选区域上进行操作,以调整候选区域的空间属性,并将调整后的候选区域分组为候选区域组,然后可以确定候选区域组中的一个或多个是否代表 在图像中存在组合的特征。
    • 10. 发明申请
    • AUTO-RECOGNITION FOR NOTEWORTHY OBJECTS
    • 自动识别注意事项
    • US20120314962A1
    • 2012-12-13
    • US13158210
    • 2011-06-10
    • Jerremy HollandJan Erik SolemWilliam E. Hensler
    • Jerremy HollandJan Erik SolemWilliam E. Hensler
    • G06K9/62
    • G06K9/00221G06F17/30247
    • Techniques for automatically identifying famous people and/or iconic images are provided. Object descriptors (or “faceprints”) of the famous people and iconic images are generated and “shipped” with a digital image management application used by an end-user. The digital image management application analyzes a digital image (generated, for example, by a digital camera operated by the end-user) to detect an object, such as a face, and generates a faceprint. The digital image management application compares the faceprint to the faceprints of the famous people and/or iconic images. If a match is found, then data that identifies the corresponding person or object is displayed to the end-user.
    • 提供了自动识别着名人物和/或标志性图像的技术。 生成着名人物和标志性图像的对象描述符(或面部图像),并附带最终用户使用的数字图像管理应用程序。 数字图像管理应用程序分析数字图像(例如由最终用户操作的数字照相机生成)以检测诸如面部的对象,并且生成面印。 数字图像管理应用程序将面部照片与着名人物和/或标志性图像的面部照片进行比较。 如果找到匹配,则向最终用户显示标识相应人物或对象的数据。