The SVM-Minus Similarity Score for Video Face Recognition

Lior Wolf, Noga Levy
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引用次数: 72

Abstract

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.
视频人脸识别的SVM-Minus相似度评分
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。
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