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    • 4. 发明授权
    • Generic face alignment via boosting
    • 通过升压进行通用面对齐
    • US08155399B2
    • 2012-04-10
    • US12056051
    • 2008-03-26
    • Xiaoming LiuPeter Henry TuFrederick Wilson Wheeler
    • Xiaoming LiuPeter Henry TuFrederick Wilson Wheeler
    • G06K9/62
    • G06K9/00241G06K9/621
    • There is provided a discriminative framework for image alignment. Image alignment is generally the process of moving and deforming a template to minimize the distance between the template and an image. There are essentially three elements to image alignment, namely template representation, distance metric, and optimization method. For template representation, given a face dataset with ground truth landmarks, a boosting-based classifier is trained that is able to learn the decision boundary between two classes—the warped images from ground truth landmarks (e.g., positive class) and those from perturbed landmarks (e.g., negative class). A set of trained weak classifiers based on Haar-like rectangular features determines a boosted appearance model. A distance metric is a score from the strong classifier, and image alignment is the process of optimizing (e.g., maximizing) the classification score. On the generic face alignment problem, the proposed framework greatly improves the robustness, accuracy, and efficiency of alignment.
    • 提供了一种用于图像对齐的辨别框架。 图像对齐通常是移动和变形模板的过程,以最小化模板和图像之间的距离。 图像对齐基本上有三个要素,即模板表示,距离度量和优化方法。 对于模板表示,给定一个具有地面真实地标的面部数据集,训练有素的分类器能够学习两个类之间的决策边界 - 来自地面真实地标(例如,积极的类)和来自扰动地标的变形图像 (例如负面班)。 基于哈尔式矩形特征的一组经过训练的弱分类器决定了外观模型的提升。 距离度量是来自强分类器的分数,图像对准是优化(例如,最大化)分类分数的过程。 在通用面对齐问题上,提出的框架大大提高了对齐的鲁棒性,准确性和效率。