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When extracting discriminative features from multi-modal 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, and employing the generalized singular value decomposition (GSVD) technique, respectively. Two typical biometric data are considered in this paper for simplicity, i.e., face data and palmprint data. Compare with several representative multimodal biometrics recognition methods, the experimental results show that the proposed SDA-GSVD based multimodal biometric feature extraction approach achieves best recognition performance.