Biometric sensor image fusion for identity verification: A case study with wavelet-based fusion rules graph matching

Abstract

Multi-biometric systems have many advantages over the uni-biometric systems. However, multi-biometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance to be degraded. This paper deals with a biometric authentication system that uses image fusion convention for face and palm-print images using wavelet decomposition. The proposed work uses a few selected wavelet fusion rules subject to fusion of biometric face and palm-print images at low-level. While fusion is accomplished with two high-resolution biometric images, SIFT operator is used to extract invariant features from spatially enhanced fused image. Finally, identity is verified by probabilistic relational graph with posteriori attributes matching between a pair of fused images. Matching is employed by searching corresponding feature points in both the database and query fused images using the iterative relaxation algorithm. The experimental results show that the proposed multimodal biometric system through image fusion outperforms feature level fusion methods, while all the fusion schemes are implemented in the same feature space, i.e., in the scale invariant feature space.