Description: When extracting discriminative features multimodal
data, current methods rarely concern the data distribution.
In this paper, we present an assumption that is consistent with
the viewpoint of discrimination, that is, a person’s overall
biometric data should be regarded as one class in the input space,
and his different biometric data can form different Gaussians
distributions, i.e., different subclasses. Hence, we propose a novel
multi-modal feature extraction and recognition approach based
on subclass discriminant analysis (SDA). Specifically, one
person’s different bio-data are treated as different subclasses of
one class, and a transformed space is calculated, where the
difference among subclasses belonging to different persons is
maximized, and the difference within each subclass is minimized.
Then, the obtained multi-modal features are used for
classification. Two solutions are presented to overcome the
singularity problem encountered in calculation, which are using
PCA preprocessing,
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