Improving the convergence of co-training for audio-visual person identification

Person identification is a very important task for intelligent devices when communicating or interacting with humans. A potential problem in real applications is that the amount of enrollment data is insufficient. When multiple modalities are available, it is possible to re-train the system online by exploiting the conditional independence between the modalities and thus improving classification accuracy. This can be achieved by the well-known CO-training algorithm [1]. In this work we present a novel modification to the CO-training algorithm, which is concerned with how new observations/samples are chosen at each iteration to re-train the system in order to improve the classification accuracy faster, i.e., better convergence. In our method, the new data are chosen not only based on the score from the other modality but also the score from the self modality. We demonstrate our proposed method on a multimodal person identification task using the MOBIO database, and show that it outperforms the baseline method, in terms of convergency, by a large margin.