Multimodal Biometric Identification Approach Based on Face and Palmprint

摘要：

Multimodal biometric identification technique utilizes two or more individual modalities to improve the identification accuracy and overcome some problems existing in conventional unimodal methods. This paper presents a multimodal biometric identification approach based on the features of face and palmprint. Two feature extraction methods are employed, one is based on the statistics properties (SP) of the biometric image and the other is the classical two-dimensional principal component analysis (2DPCA). The minimal distance rule (MDR) is adopted for fusion at the matching score level. We compare the results of the multimodality identification with the results of the unimodal face and palmprint identification. The experimental results show that the performance of multimodality outperforms the unimodal identification and the accuracy can reach 100％ based on ORL face database and PolyU palmprint database using the fusion rule at the matching score level.

关键词： Multimodal biometrics SP 2DPCA MDR

作者: Cheng Lu Jisong Wang Miao Qi

作者单位: Computer School Jilin University Changchun, China Computer School Northeast Normal University Changchun, China

会议类型: 国际会议

会议名称: Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)

会议地点: 南昌

会议语种:英文

页码: 700-703

在线出版日期: 2009-05-22（万方平台首次上网日期，不代表论文的发表时间）