Abstract
This paper develops a new image feature extraction and recognition method coined bidirectional compressed nuclear-norm based 2DPCA (BN2DPCA). BN2DPCA presents a sequentially optimal image compression mechanism, making the information of the image compact into its up-left corner. BN2DPCA is tested using the Extended Yale B and the CMU PIE face databases. The experimental results show that BN2DPCA is more effective than N2DPCA, B2DPCA, LPP and LDA for face feature extraction and recognition.
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Ding, Y., Chen, C., Gu, Y., Wang, Y. (2014). Nuclear Norm Based Bidirectional 2DPCA. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_47
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DOI: https://doi.org/10.1007/978-3-319-12484-1_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
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