Abstract
For multimodal biometric person recognition, information fusion can be classified into several levels: rank, decision, sensor, feature and match-score levels. In this paper, a novel method is proposed to fuse information from two or more biometric sources at feature fusion level. A key aspect of the method is to use an optimisation procedure to regulate the contribution of each individual biometric modality to the concatenated feature vector. As an example, the effectiveness of the method is demonstrated by integrating features of static face images and text-independent speech segments. Experiments in feature-level fusion are carried out for 40 subjects from a virtual database consisting of face images and speech clips, and the results show that the proposed method outperforms those without feature fusion and those based on intuition feature fusion.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.