Transfer Learning of Structured Representation for Face Recognition

Face recognition under uncontrolled conditions, e.g., complex backgrounds and variable resolutions, is still challenging in image processing and computer vision. Although many methods have been proved well-performed in the controlled settings, they are usually of weak generality across different data sets. Meanwhile, several properties of the source domain, such as background and the size of subjects, play an important role in determining the final classification results. A transferrable representation learning model is proposed in this paper to enhance the recognition performance. To deeply exploit the discriminant information from the source domain and the target domain, the bioinspired face representation is modeled as structured and approximately stable characterization for the commonality between different domains. The method outputs a grouped boost of the features, and presents a reasonable manner for highlighting and sharing discriminant orientations and scales. Notice that the method can be viewed as a framework, since other feature generation operators and classification metrics can be embedded therein, and then, it can be applied to more general problems, such as low-resolution face recognition, object detection and categorization, and so forth. Experiments on the benchmark databases, including uncontrolled Face Recognition Grand Challenge v2.0 and Labeled Faces in the Wild show the efficacy of the proposed transfer learning algorithm.