Home > Research > Publications & Outputs > Face recognition using kernel principal compone...

Electronic data


Text available via DOI:

View graph of relations

Face recognition using kernel principal component analysis

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>2002
<mark>Journal</mark>IEEE Signal Processing Letters
Issue number2
Number of pages3
Pages (from-to)40-42
Publication StatusPublished
<mark>Original language</mark>English


A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features. Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.