Abstract
Face recognition is commonly used for biometric security purposes in video surveillance and user authentications. The nature of face exhibits non-linear shapes due to appearance deformations, and face variations presented by facial expressions. Recognizing faces reliably across changes in facial expression has proved to be a more difficult problem leading to low recognition rates in many face recognition experiments. This is mainly due to the tens degree-of-freedom in a non-linear space. Recently, non-linear PCA has been revived as it posed a significant advantage for data representation in high dimensionality space. In this paper, we experimented the use of non-linear kernel approach in 3D face recognition and the results of the recognition rates have shown that the kernel method outperformed the standard PCA.
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Acknowledgement
The authors would like to thank Suriani Ab Rahman, Phoon Jai Hui for contributing to the research in this paper, and Newton Fund for the financial support for the publication.
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Peter, M., Minoi, JL., Hipiny, I.H.M. (2019). 3D Face Recognition using Kernel-based PCA Approach. In: Alfred, R., Lim, Y., Ibrahim, A., Anthony, P. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 481. Springer, Singapore. https://doi.org/10.1007/978-981-13-2622-6_8
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DOI: https://doi.org/10.1007/978-981-13-2622-6_8
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