Multimodal biometrics recognition based on local fusion visual features and variational Bayesian extreme learning machine. (1st December 2016)

• Record Type: Journal Article
• Authors: Chen, Yarui; Yang, Jucheng; Wang, Chao; Liu, Na
• Is Part Of: Expert systems with applications. Volume 64(2016)
• Page Start: 93
• Page End: 103
• Publication Date: 2016-12-01
• Subjects: Multimodal biometrics recognition -- Local fusion visual feature -- Extreme learning machine -- Variational Bayesian; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.07.009
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new approach for multimodal biometrics recognition is proposed. Local fusion visual feature has better characterization capability. Variational Bayesian ELM provides superior performance with full Bayesian prior. Variational technology solves the automatic selection problem of hidden nodes in ELM. Abstract: Multimodal biometrics provides rich information in biometric recognition systems, thus a valid multimodal feature fusion framework and an efficient recognition algorithm are desirable for multimodal biometrics systems. In this paper, we design a multimodal fusion framework for face and fingerprint images using block based feature-image matrix, and extract a type of middle-layer semantic feature from local features—a local fusion visual feature, which has better characterization capabilities with lower dimension for multimodal biometrics. Furthermore, we create recognition utilizing the Variational Bayesian Extreme Learning Machine (VBELM), which has an obvious speed advantage by random input weights, and also has superior stability and generalization by adding a non-informative full Gaussian prior. This research enables multimodal biometrics recognition system to have a concentrated fusion feature description and great recognition performance. Experimental results show that the proposed multimodal biometrics recognition system has a higher testing accuracy in comparison to the traditional methods with higher efficiency and better stability.