Incremental Learning Techniques Within a Self-updating Approach for Face Verification in Video-Surveillance

摘要

Data labelling is still a crucial task which precedes the training of a face verification system. In contexts where training data are obtained online during operational stages, and/or the genuine identity changes over time, supervised approaches are less suitable. This work proposes a face verification system capable of autonomously generating a robust model of a target identity (genuine) from a very limited amount of labelled data (one or a few video frames). A self-updating approach is used to wrap two well known incremental learning techniques, namely Incremental SVM and Online Sequential ELM. The performance of both strategies are compared by measuring their ability to unsupervisedly improve the model of the genuine identity over time, as the system is queried by both genuine and impostor identities. Results confirm the feasibility and potential of the self-updating approach in a video-surveillance context.