In this paper, we propose a novel online learning method called Multi-Eigenspace Learning which can learn appearance models incrementally from a given video stream. For each subject, we try to learn a few eigenspace models using IPCA (Incremental Principal Component Analysis). In the process of Multi-Eigenspace Learning, each eigenspace generally contains more and more samples except one eigenspace which contains the least number of samples. Then, these learnt eigenspace models are used for video-based face recognition. Experimental results show that the proposed method can achieve high recognition rate. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Liu, L., Wang, Y., & Tan, T. (2007). Multi-eigenspace learning for video-based face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4642 LNCS, pp. 181–190). Springer Verlag. https://doi.org/10.1007/978-3-540-74549-5_20
Mendeley helps you to discover research relevant for your work.