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Robust Face Recognition after Plastic Surgery Using Local Region Analysis

机译:基于局部区域分析的整形外科手术后人脸识别

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Face recognition in real-world applications is often hindered by uncontrolled settings including pose, expression, and illumination changes, and/or ageing. Additional challenges related to changes in facial appearance due to plastic surgery have become apparent recently. We exploit the fact that plastic surgery bears on appearance in a non-uniform fashion using a recognition approach that integrates information derived from local region analysis. We implemented and evaluated the performance of two new integrative methods, FARO and FACE, which are based on fractals and a localized version of a correlation index, respectively Experimental results confirm the expectation that face recognition is indeed challenged by the effects of plastic surgery. The same experimental results also show that both FARO and FACE compare favourably against standard face recognition methods such as PCA and LDA.
机译:实际应用中的人脸识别通常会受到不受控制的设置(包括姿势,表情和光照变化和/或老化)的阻碍。最近,与整形外科手术引起的面部外观变化有关的其他挑战变得显而易见。我们利用这样的事实,即整形外科采用一种识别方法,以一种不均匀的方式出现在外观上,该方法整合了从局部区域分析得出的信息。我们分别基于分形和相关指数的局部版本,实施并评估了两种新的集成方法FARO和FACE的性能。实验结果证实了人们的期望,即面部识别确实受到整形外科手术的挑战。相同的实验结果还表明,FARO和FACE与标准人脸识别方法(例如PCA和LDA)相比均具有优势。

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