Abstract
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.
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Gu, X., Liu, C., Wang, S. (2013). Supervised Slow Feature Analysis for Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_22
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DOI: https://doi.org/10.1007/978-3-319-02961-0_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
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