  The problem of image-base person identification/recognition is to provide an
identity to the image of an individual based on learned models that describe
his/her appearance. Most traditional person identification systems rely on
learning a static model on tediously labeled training data. Though labeling
manually is an indispensable part of a supervised framework, for a large scale
identification system labeling huge amount of data is a significant overhead.
For large multi-sensor data as typically encountered in camera networks,
labeling a lot of samples does not always mean more information, as redundant
images are labeled several times. In this work, we propose a convex
optimization based iterative framework that progressively and judiciously
chooses a sparse but informative set of samples for labeling, with minimal
overlap with previously labeled images. We also use a structure preserving
sparse reconstruction based classifier to reduce the training burden typically
seen in discriminative classifiers. The two stage approach leads to a novel
framework for online update of the classifiers involving only the incorporation
of new labeled data rather than any expensive training phase. We demonstrate
the effectiveness of our approach on multi-camera person re-identification
datasets, to demonstrate the feasibility of learning online classification
models in multi-camera big data applications. Using three benchmark datasets,
we validate our approach and demonstrate that our framework achieves superior
performance with significantly less amount of manual labeling.
