Ieee Transactions on Pattern Analysis and Machine Intelligence Describable Visual Attributes for Face Verification and Image Search
Ieee Transactions on Pattern Analysis and Machine Intelligence Describable Visual Attributes for Face Verification and Image Search
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通讯作者：
Neeraj Kumar;A. Berg;P. Belhumeur;S. Nayar
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作者：
Neeraj Kumar;A. Berg;P. Belhumeur;S. Nayar
-介绍使用可描述的视觉属性进行人脸验证和图像搜索。可描述的视觉属性是可以赋予图像以描述其外观的标签。本文的重点是人脸图像和用于描述它们的属性，尽管这些概念也适用于其他领域。面部属性的例子包括性别、年龄、下巴形状、鼻子大小等。基于属性的视觉任务表示的优点是多方面的：它们可以组成不同层次的特异性描述；它们是可概括的，因为它们可以学习一次，然后应用于识别新的对象或类别，而无需进一步的训练；而且它们是高效的，可能比显式命名每个类别所需的属性（和训练数据）要少得多。我们展示了如何创建和标记现实世界图像的大型数据集来训练分类器，这些分类器测量图像中属性表达的存在，不存在或程度。然后，这些分类器可以自动标记新图像。我们通过人类和计算实验证明了使用属性进行人脸验证和图像搜索的当前有效性，并探索了未来的潜力。最后，我们引入了两个新的人脸数据集，分别称为FaceTracer和PubFig，它们具有标记的属性和身份。
—We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: they can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large datasets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness – and explore the future potential – of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face datasets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.