Person-specific domain adaptation with applications to heterogeneous face recognition
Heterogeneous face recognition (HFR) is a practical yet challenging task in which gallery and probe face images are collected in terms of different modalities or features (e.g., sketch vs. photo). In this paper, we present a person-specific domain adaptation framework for HFR. By utilizing the subjects not of interest (i.e., those not to be recognized), we first derive a common feature space using their cross-domain face images, with the goal of eliminating differences between image modalities. To generalize our feature space for representing and recognizing the subjects of interest, we advocate the construction of person-specific domain adaptation model in this space, so that the classifiers (trained by the gallery images) are able to achieve satisfactory recognition performance. In our experiments, we consider sketch-to-photo and near-infrared (NIR) to visible spectrum (VIS) face recognition problems for evaluating the effectiveness of our method.