Abstract
Traditional linear discriminant analysis is very sensitive to largely corrupted data. To address this problem, based on the recent success of low-rank matrix recovery, the paper proposes a novel low-rank constrained linear discriminant analysis (LRLDA) algorithm for head pose estimation and face recognition. By adding the low-rank constraint in our method, LRLDA can obtain more robustness and discriminating power compared with traditional LDA algorithms. The extensive experimental results demonstrate the effectiveness of LRLDA.
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Yi, S., Chen, C., Cui, J. (2013). Low-Rank Constrained Linear Discriminant Analysis. 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_13
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DOI: https://doi.org/10.1007/978-3-319-02961-0_13
Publisher Name: Springer, Cham
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