A Fast and Robust Negative Mining Approach for Enrollment in Face Recognition Systems

Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose here a first solution for this problem, which (i) limits the number of negative samples in the training set for preserving responsiveness during user enrollment, (ii) selects the most informative negative samples with respect to each user for preserving accuracy, and (iii) builds a user-specific classification model. We combine a high-dimensional data representation from deep learning with a method that selects negative samples from a large mining set and builds, within interactive times, effective user-specific training set and classifier, using linear support vector machines. The method can also be used with other feature extractors. It has shown superior performance as compared to five baseline methods on three unconstrained data sets.