Correlation-based identification approach for multimodal biometric fusion

doi:10.1016/S1005-8885(17)60221-8

中国邮电高校学报(英文) ›› 2017, Vol. 24 ›› Issue (4): 34-39.doi: 10.1016/S1005-8885(17)60221-8

• Artificial Intelligence • 上一篇下一篇

Correlation-based identification approach for multimodal biometric fusion

马欣¹,景晓军²

1. 北京邮电大学
2. 北京邮电大学研究生院信息与通信学院

收稿日期:2017-03-22 修回日期:2017-06-09 出版日期:2017-08-30 发布日期:2017-08-30
通讯作者: 马欣 E-mail:xinma@bupt.edu.cn

Correlation-based identification approach for multimodal biometric fusion

Received:2017-03-22 Revised:2017-06-09 Online:2017-08-30 Published:2017-08-30
Contact: Xin MA E-mail:xinma@bupt.edu.cn

摘要/Abstract

摘要： Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains richer information about the biometric data. In this paper, we present a multiset generalized canonical discriminant projection (MGCDP) method for feature-level multimodal biometric information fusion, which maximizes the correlation of the intra-class features while minimizes the correlation of the between-class. In addition, the serial MGCDP (S-MGCDP) and parallel MGCDP (P-MGCDP) strategy were also proposed, which can fuse more than two kinds of biometric information, so as to achieve better identification effect. Experiments performed on various biometric databases shows that MGCDP method outperforms other state-of-the-art feature-level information fusion approaches.

关键词: correlation analysis, multimodal biometric information, information fusion

Abstract: Information fusion is a key step in multimodal biometric systems. The feature-level fusion is more effective than the score-level and decision-level method owing to the fact that the original feature set contains richer information about the biometric data. In this paper, we present a multiset generalized canonical discriminant projection (MGCDP) method for feature-level multimodal biometric information fusion, which maximizes the correlation of the intra-class features while minimizes the correlation of the between-class. In addition, the serial MGCDP (S-MGCDP) and parallel MGCDP (P-MGCDP) strategy were also proposed, which can fuse more than two kinds of biometric information, so as to achieve better identification effect. Experiments performed on various biometric databases shows that MGCDP method outperforms other state-of-the-art feature-level information fusion approaches.

Key words: correlation analysis, multimodal biometric information, information fusion

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Correlation-based identification approach for multimodal biometric fusion

Correlation-based identification approach for multimodal biometric fusion

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