Dictionary-based domain adaptation methods for the re-identification of faces

Re-identification refers to the problem of recognizing a person at a different location after one has been captured by a camera at a previous location. We discuss re-identification of faces using the domain adaptation approach which tackles the problem where data in the target domain (different location) are drawn from a different distribution as the source domain (previous location), due to different view points, illumination conditions, resolutions, etc. In particular, we discuss the adaptation of dictionary-based methods for re-identification of faces. We first present a domain adaptive dictionary learning (DADL) framework for the task of transforming a dictionary learned from one visual domain to the other, while maintaining a domain-invariant sparse representation of a signal. Domain dictionaries are modeled by a linear or nonlinear parametric function. The dictionary function parameters and domain-invariant sparse codes are then jointly learned by solving an optimization problem. We then discuss an unsupervised domain adaptive dictionary learning (UDADL) method where labeled data are only available in the source domain. We propose to interpolate subspaces through dictionary learning to link the source and target domains. These subspaces are able to capture the intrinsic domain shift and form a shared feature representation for cross-domain identification.