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Deceiving faces: When plastic surgery challenges face recognition

机译：变脸：当整容手术挑战时会变脸

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An exponential growth of the number of plastic surgery treatments specific to face (from the minimally-invasive ones to the real surgical procedures) has characterized the last two decades and it seems likely that this phenomenon, that has social and cultural meanings and implications, could spread even further in the next years as the average cost of these treatments is lowering and the wish for "beautification" is becoming part of the global esthetics sense. For these reasons, face recognition as an established research topic has a new major challenge: delivering methods capable of high recognition accuracy even in case probe and gallery differ by a surgical alteration of face shape. To this aim is of fundamental importance understanding the range and the extent of the modification produced by the various types of treatments or by a combination of them. We present a survey of the state of the art on this topic, starting by an analysis of the diffusion of the facial plastic surgery and describing the key aspects of each of the most statistically relevant treatments available, resumed by a synthetic table. The paper includes a brief description of all the approaches proposed in the field so far to the best of authors' knowledge and a comparison of the performance reported by the existing methods when applied to the most referenced plastic surgery face dataset to date. A critical discussion of the results achieved so far and an insight about the challenges that still have to be addressed concludes this work. (C) 2016 Elsevier B.V. All rights reserved.
机译：在过去的二十年中，针对面部的整形外科治疗的数量呈指数增长（从微创治疗到实际的外科手术），这种现象具有社会和文化意义并具有一定的意义。随着这些疗法的平均成本降低，并且在未来几年中，这种趋势将进一步蔓延，“美化”的愿望已成为全球美学的一部分。由于这些原因，将面部识别作为既定的研究课题面临着新的主要挑战：即使在由于面部形状的手术改变而导致探头和长廊不同的情况下，提供能够实现高识别精度的方法。为此目的，至关重要的是理解由各种类型的处理或它们的组合产生的修饰的范围和程度。我们对这一主题进行了最新的调查，首先是对面部整形外科手术的扩散进行分析，并描述了每种可用的与统计最相关的治疗方法的关键方面，并通过综合表进行了介绍。这篇论文包括到目前为止，就作者所知，该领域中提出的所有方法的简要说明，以及当将现有方法应用于迄今为止引用最多的整形外科面部数据集时，对现有方法所报告的性能进行比较。到目前为止，对所取得的成果进行了严格的讨论，并对仍需解决的挑战进行了深入了解，从而完成了这项工作。 （C）2016 Elsevier B.V.保留所有权利。

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