ISSN : 1796-203X
Volume : 3    Issue : 10    Date : October 2008

Accelerated Kernel CCA plus SVDD: A Three-stage Process for Improving Face Recognition
Ming Li and Yuanhong Hao
Page(s): 94-100
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Kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning
methods, which shows to be a powerful approach of extracting nonlinear features for face
classification and other applications. However, the standard KCCA algorithm may suffer from
computational problem as the training set increase. To overcome the drawback, we propose a
threestage method to improve the performance of KCCA. Firstly, a scheme based on geometrical
consideration is proposed to enhance the extraction efficiency. The algorithm can select a subset of
samples whose projections in feature space (Hilbert space) are sufficient to represent all of the
data in feature space. Subsequently, an improved algorithm inspired by principal component
analysis (PCA) is developed. The algorithm can select the most contributive eigenvectors for
training and classification instead of considering all the ones. Finally, a multi-class classification
method based on support vectors data description (SVDD) is employed to further enhance the
recognition performance as it can avoid the repeated use of training data. The theoretical analysis
and the experiment results demonstrate the effectiveness of improvements.

Index Terms
face recognition, kernel canonical correlation analysis, feature vector selection (FVS), support
vectors data description (SVDD)