The SVM-Minus Similarity Score for Video Face Recognition
The SVM-Minus Similarity Score for Video Face Recognition
复制标题
DOI：
10.1109/cvpr.2013.452
复制
发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
Lior Wolf;Noga Levy
中科院分区：
文献类型：
--
作者：
Lior Wolf;Noga Levy
挑战，但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，图像分析工具可以提供准确的估计。我们提出的方法属于一个家庭的基于分类器的相似性分数。我们提出了一种有效的方法来折扣构成诱导相似性在这样的框架内，这是基于一个新引入的分类称为SVM-减。所提出的方法被证明优于现有技术的最具挑战性和现实的公开可用的视频人脸识别基准，无论是本身，并与其他方法相结合。
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.