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A sparse representation approach to face matching across plastic surgery

机译:整形外科中用于面部匹配的稀疏表示方法

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Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. In this paper, we propose a novel approach to address the challenges involved in automatic matching of faces across plastic surgery variations. In the proposed formulation, part-wise facial characterization is combined with the recently popular sparse representation approach to address these challenges. The sparse representation approach requires several images per subject in the gallery to function effectively which is often not available in several use-cases, as in the problem we address in this work. The proposed formulation utilizes images from sequestered non-gallery subjects with similar local facial characteristics to fulfill this requirement. Extensive experiments conducted on a recently introduced plastic surgery database [17] consisting of 900 subjects highlight the effectiveness of the proposed approach.
机译:整形外科手术可以显着改变面部外观,从而甚至对最先进的面部匹配算法也构成了严峻挑战。在本文中,我们提出了一种新颖的方法来应对整形外科变异中的面部自动匹配所涉及的挑战。在提出的配方中,部分面部特征与最近流行的稀疏表示方法相结合,以解决这些挑战。稀疏表示方法要求画廊中每个主题有效地运行几个图像,而这在多个用例中通常是不可用的,正如我们在本工作中要解决的问题。拟议的配方利用了来自隔离的非画廊对象的图像,这些对象具有相似的局部面部特征来满足此要求。在最近引入的整形外科数据库[17]上进行的广泛实验,由900名受试者组成,凸显了该方法的有效性。

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