Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches. 1. Introduction With the advantage of reliability and stability, biometric recognition has been developing rapidly for security and personal identity recognition. Protecting resources from an intruder is a crucial problem for the owner. The multimodal biometric system integrates more biometrics to improve security and accuracy and hence is capable of handling more efficiently the nonuniversality problem of human traits. In fact, it is very common to use a variety of biological characteristics for identification. In fact, it is very common to use a variety of biological characteristics for identification because different biological characteristics are knowingly/unknowingly used by people to identify a person. Fusion of multiple biometric traits provides more useful information compared to that obtained using unimodal biometric trait. Use of different feature extraction techniques from each modality possibly covers some features those are not captured by the first method. Supplementary information on the same identity helps in achieving high performance [1]. Prevailing practices in multimodal fusion are broadly categorized as prematching and postmatching fusion [2]. Feature level fusion is prematching activity. Fusion at the feature level
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