文件名称:06094337
- 所属分类：
- 技术管理
- 资源属性：
- [PDF]
- 上传时间：
- 2016-01-29
- 文件大小：
- 575kb
- 下载次数：
- 0次
- 提 供 者：
- avinash********
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
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,
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,
(系统自动生成,下载前可以参看下载内容)
下载文件列表
06094337.pdf