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06094337

  • Category : Technology Management
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  • Update : 2016-01-29
  • Size : 575kb
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  • Author :avinash********
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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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