Kernel mutual subspace method and its application for object recognition.

In this paper, the authors propose a new object recognition algorithm called the kernel mutual subspace method. The mutual subspace method proposed by Maeda is a superior technique for implementing robust object recognition by performing a principal component analysis on multiple input images. However, like with the ordinary subspace method, a shortcoming of this technique is that performance deteriorates when the category distribution has a nonlinear structure. To solve this problem, the authors theoretically derived a new object recognition algorithm called the kernel mutual subspace method by applying the kernel nonlinear principal component analysis, which is known as a powerful nonlinear principal component analysis method, to the mutual subspace method. When the proposed technique was applied to an individual identification problem based on facial images, it was apparent that the relationship between the degrees of freedom of the object motion and the subspace dimensionality indicating a high recognition rate could be consistently explained through experiments that used the proposed method, which did not differ significantly from the conventional method at the highest precision. They also showed that the proposed technique could be effective for large-scale recognition problems and that its recognition dictionary has a more compact structure. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(6): 45–53, 2005; Published online in Wiley InterScience (<URL>www.interscience.wiley.com</URL>). DOI 10.1002/ecjb.20190 [ABSTRACT FROM AUTHOR]