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发帖数: 32 | 1 请有兴趣且方向适合的站内联系，请附上姓名及研究方向、发表文章等简要介绍。谢谢。
"Efficient Kernel-based 2DPCA for Smile Stages Recognition"
Abstract
Recently, an approach called two-dimensional principal component analysis
(2DPCA) has been proposed for smile stages representation and recognition.
The essence of 2DPCA is that it computes the eigenvectors of the so-called
image covariance matrix without matrix-to-vector conversion so the size of
the image covariance matrix are much smaller, easier to evaluate covariance
matrix, computation cost is reduced and the performance is also improved
than traditional PCA. In an effort to improve and perfect the performance of
smile stages recognition, in this paper, we propose efficient Kernel based
2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the
nonlinear structures in the input data. This paper discusses comparison of
standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile
stages recognition. The results of experiments show that Kernel based 2DPCA
achieve better performance in comparison with the other approaches. While
the use of efficient Kernel based 2DPCA can speed up the training procedure
of standard Kernel based 2DPCA thus the algorithm can achieve much more
computational efficiency and remarkably save the memory consuming compared
to the standard Kernel based 2DPCA. |
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