视频人脸识别的SVM-Minus相似度评分

Lior Wolf, Noga Levy
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引用次数: 72

摘要

挑战,也是消除虚假相似性的机会。幸运的是,面部视觉相似性的一个主要混淆来源是3D头部方向,图像分析工具提供了准确的估计。我们提出的方法属于一类基于分类器的相似度评分。我们提出了一种有效的方法,在这样的框架内折扣姿态引起的相似性,这是基于一个新引入的分类器称为SVM-minus。在最具挑战性和最现实的公开视频人脸识别基准上,所提出的方法被证明优于现有技术,无论是单独使用还是与其他方法协同使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The SVM-Minus Similarity Score for Video Face Recognition
Challenge, but also an opportunity to eliminate spurious similarities. Luckily, a major source of confusion in visual similarity of faces is the 3D head orientation, for which image analysis tools provide an accurate estimation. The method we propose belongs to a family of classifier-based similarity scores. We present an effective way to discount pose induced similarities within such a framework, which is based on a newly introduced classifier called SVM-minus. The presented method is shown to outperform existing techniques on the most challenging and realistic publicly available video face recognition benchmark, both by itself, and in concert with other methods.
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