Incremental update of biometric models in face-based video surveillance

Video-based face recognition of individuals involves matching facial regions captured in video sequences against the model of individuals enrolled to a face recognition system. Due to a limited control over operational conditions, classification systems applied to face matching are confronted with complex pattern recognition environments that change over time. Therefore, the facial model of an individual tends to diverge from the underlying data distribution. Although a limited amount of reference data is often collected during initial enrollment, new samples often become available over time to update and refine models. In this paper, an adaptive ensemble of classifiers is proposed to update facial models in response to new reference samples. To avoid knowledge corruption linked to incremental learning of monolithic classifiers, and maintain a high level of performance, this ensemble exploits a learn-and-combine approach. In response to new reference samples, a new 2-class Probabilistic Fuzzy ARTMAP classifier is trained and combined to previously-trained classifiers in the ROC space. Iterative Boolean Combination is employed for fusion of 2-class classifiers of each individual in the decision space. Performance is assessed in terms of AUC accuracy and resource requirements under different incremental learning scenarios with new data extracted from the Faces in Action data set. Simulation results indicate that the proposed system significantly outperforms reference classifiers and ensembles for incremental learning.