Incremental multi-model dictionary learning for face tracking
Aasim Khurshid, Jacob Scharcanski · 2018
In this work, a new method based on a multi-model dictionary is proposed for face tracking. A reconstruction and a classification dictionary are combined, and each dictionary is learned from positive and negative examples. This scheme tends to enhance the discrimination between a tracked target face and the background. Also, an efficient scheme that collects data during face tracking is proposed to update the dictionaries in an incremental learning scheme, allowing to track faces even when the face appearance changes (e.g. under different face expressions). The preliminary experimental results suggest that the proposed method tends to perform better than comparative methods, which are representative of the state-of-the-art.