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Abstract:
Principal Component Analysis and Fisher Linear Discriminant
methods have demonstrated their success in face detection,
recognition, and tracking. The representation in these subspace
methods is based on second order statistics of the image set, and
does not address higher order statistical dependencies such as
the relationships among three or more pixels. Recently Higher
Order Statistics and Independent Component Analysis (ICA) have
been used as informative low dimenstional representations for
visual recognition. In this paper, we investigate the use of
Kernel Principal Component Analysis and Kernel Fisher Linear
Discriminant for learning low dimensional representations for
face recognition. While Eigenface and Fisherface methods aim to
find projection directions based on the second order correlation
of samples, Kernel Eigenface and Kernel Fisherface methods
provide generalizations which take higher order correlations into
account. We compare the performance of kernel methods with
Eigenface, Fisherface and ICA-based methods for face recognition
with variation in pose, scale, lighting and expression.
Experimental results show that kernel methods provide better
representations and achive lower error rates for face
recognition.
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