Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition
Recognizing image data across different domains has been a challenging task. For biometrics, heterogeneous face recognition (HFR) deals with recognition problems in which training/gallery images are collected in terms of one modality (e.g., photos), while test/probe images are observed in the other (e.g., sketches). In this paper, we present a domain adaptation approach for solving HFR problems. By utilizing external face images (i.e., those collected from the subjects not of interest) from both source and target domains, we propose a novel Domain-independent Component Analysis (DiCA) algorithm for deriving a common subspace for relating and representing cross-domain image data. In order to introduce improved representation ability, we further advance the self-taught learning strategy for learning a domain-independent dictionary in our DiCA subspace, which can be applied to both gallery and probe images of interest to improve representation and recognition. Different from some prior domain-adaptation approaches, we do not require the data correspondences (i.e., data pairs) when collecting external cross-domain image data, nor the label information is needed for learning the common feature space when associating different domains. Thus, our method is practical for real-world cross-domain classification problems. In our experiments, we consider sketch-to-photo and near-infrared (NIR) to visible spectrum (VIS) face recognition problems for evaluating the performance of our proposed approach.