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,-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,