Abstract
For the single training sample per person (SSPP) problem, this paper proposes an adaptive weighted LTP algorithm with a novel weighted method involving the standard deviation of the sub-images’ feature histogram. First, LTP operator is used to extract texture feature and then feature images are split into sub images. Then, standard deviation is used to compute the adaptive weighted fusion of features. Finally, the nearest classifier is adopted for recognition. The experiments on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.
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© 2013 Springer International Publishing Switzerland
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Huang, R., Zhu, L., Yang, W., Zhang, B., Sun, C. (2013). An Improved Adaptive Weighted LTP Algorithm for Face Recognition Based on Single Training Sample. 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_2
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DOI: https://doi.org/10.1007/978-3-319-02961-0_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
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