Semi Supervised Learning in Wild Faces and Videos
David Rim, Kamrul Hassan, Chris Pal, Ecole-Polytechnique Montreal · 2011
We propose an approach for improving unconstrained face recognition based on leveraging weakly labeled web videos. It is easy to obtain videos that are likely to contain a face of interest from sites such as YouTube through issuing queries with a person’s name; however, many examples of faces not belonging to the person of interest will be present. We propose a new technique capable of learning using weakly or noisly labeled faces obtained in this setting. In particular, we present a novel method for semi-supervised learning using noisy labels which incorporates a margin or null category like property within a fully probabilistic framework. We outline general properties of the approach, showing how the choice of an exponential hyperprior results in an L1 penality which leads to sparse models capable of explicitly accounting for label uncertainty producing state of the art performance. We then illustrate how the margin approach provides robustness and significant performance gains when faces within YouTube search results are combined with the unconstrained face images from the Labeled Faces in the wild dataset.