Multimodal Biometric Verification using the Iris and Major Finger Knuckles
| dc.contributor.author | Abderrahmane Herbadji | |
| dc.contributor.author | Noubeil Guermat | |
| dc.contributor.author | Lahcene Ziet | |
| dc.contributor.author | Mohamed Cheniti | |
| dc.date.accessioned | 2021-05-20T08:02:38Z | |
| dc.date.available | 2021-05-20T08:02:38Z | |
| dc.date.issued | 2021 | |
| dc.description.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% | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/24305 | |
| dc.subject | Biometrics; Multibiometrics; Grouping function; Score level fusion; Authentication; Iris; Major finger Knuckles | en_US |
| dc.title | Multimodal Biometric Verification using the Iris and Major Finger Knuckles | en_US |
| dc.type | Article | en_US |