A robust biometrics multilevel fusion scheme. (2016)

• Record Type: Journal Article
• Authors: Chaudhary, Gopal; Srivastava, Smriti; Bhardwaj, Saurabh
• Is Part Of: International journal of image mining. Volume 2:Number 2(2016)
• Page Start: 100
• Page End: 115
• Publication Date: 2016
• Subjects: feature extraction -- fusion -- identi?cation -- multimodal -- noise; Image processing -- Periodicals; Data mining -- Periodicals; 006.42
• Languages: English
• ISSNs: 2055-6039

Abstract

In this paper, the use of multimodal biometrics is suggested over unimodal biometric system due to the inherent disadvantages of unimodal system. Two or more biometric modalities when combined, offer more reliable identification and verification system. Here, a novel multilevel fusion of palm print and dorsal hand vein is proposed. To fuse the biometric modalities, various levels of fusion are reviewed. In the proposed multilevel fusion, left-and right-hand palm prints of IIT Delhi palmprint database are fused using feature-level fusion to get feature fused vector (FFV). In this, novel feature-level fusion rules are suggested to spatially combine the information of the data samples. These rules are also useful to control the dimension of fused features. After feature-level fusion, features from dorsal hand vein from Bosphorus hand vein database are combined with FFV using score-level fusion. Hence both the feature-level as well as score-level fusion techniques have been used one after the other. The improvement of results verifies the success of our approach. To validate the robustness of proposed multilevel fusion, noise at different level of intensity is artificially added to the test samples. The results suggested the strength of multilevel fusion scheme.