Efficient Kernel-based Two-Dimensional Principal Component Analysis 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.
Author: Rima Tri Wahyuningrum,
Fitri Damayanti
Journal Code: jptkomputergg120031