Noga Levy (Tel-Aviv University)
Tuesday, 23.4.2013, 11:30
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 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.