Authors: Lin Huang, Hanqi Zhuang, Salvatore D. Morgera
Addresses: Department of Electrical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. ' Department of Electrical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. ' Department of Electrical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
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.
Keywords: information fusion; sensor fusion; feature fusion; multimodal biometrics; person recognition; face images; speech segments.
International Journal of Biometrics, 2009 Vol.1 No.4, pp.479 - 494
Published online: 19 Jul 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article