Multimodal Biometric Verification using the Iris and Major Finger Knuckles
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Date
2021
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Abstract
The drawbacks of unimodal biometric systems
such as non-universality, noisy sensor data and spoofing can be
mitigated using multiple biometric traits. In this study, a novel
multibiometric system to authenticate users based on their
major knuckle finger patterns using four fingers (i.e., little, ring,
middle, and index) and iris is proposed. A local texture
descriptor namely binarized statistical image features (BSIF) has
been employed to extract the features for each of the biometric
traits considered in order to improve biometric-based personal
verification. The comparison results on PolyU contactless hand
dorsal images database and IIT Delhi-1 iris database indicate
that the proposed multibiometric authentication with grouping
function based score fusion outperforms the existing
transformation-based fusion approaches in literature (e.g., tnorms,
symmetric-sum), attaining a correct recognition rate of
95.54%
Description
Keywords
Biometrics; Multibiometrics; Grouping function; Score level fusion; Authentication; Iris; Major finger Knuckles