Slow feature analysis (SFA) is a new method based on the slowness principle and extracts slowly varying signals out of the input data. However, traditional SFA cannot be directly performed on those dataset without an obvious temporal structure. In this paper, a novel supervised slow feature analysis (SSFA) is proposed, which constructs pseudo-time series by taking advantage of the consensus information. Extensive experiments on AR and PIE face databases demonstrate superiority of our proposed method. © Springer International Publishing 2013.
CITATION STYLE
Gu, X., Liu, C., & Wang, S. (2013). Supervised slow feature analysis for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8232 LNCS, pp. 178–184). https://doi.org/10.1007/978-3-319-02961-0_22
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