The SVM-Minus Similarity Score for Video Face Recognition

Lior Wolf, Noga Levy; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3523-3530

Abstract


Face recognition in unconstrained videos requires specialized tools beyond those developed for still images: the fact that the confounding factors change state during the video sequence presents a unique 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 classifierbased 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 SVMminus. 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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[bibtex]
@InProceedings{Wolf_2013_CVPR,
author = {Wolf, Lior and Levy, Noga},
title = {The SVM-Minus Similarity Score for Video Face Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}