Discriminative training of hyper-feature models for object identification

Object identification is the task of identifying specific objects belonging to the same class such as cars. We often need to recognize an object that we have only seen a few times. In fact, we often observe only one example of a particular object before we need to recognize it again. Thus we are interested in building a system which can learn to extract distinctive markers from a single example and which can then be used to identify the object in another image as “same ” or “different”. Previous work by Ferencz et al. introduced the notion of hyper-features, which are properties of an image patch that can be used to estimate the utility of the patch in subsequent matching tasks. In this work, we show that hyper-feature based models can be more efficiently estimated using discriminative training techniques. In particular, we describe a new hyper-feature model based upon logistic regression that shows improved performance over previously published techniques. Our approach significantly outperforms Bayesian face recognition that is considered as a standard benchmark for face recognition