Paper Info
Title
A Two-Stage Scheme for Fusion of Hash-Encoded Features in a Multimodal Biometric System
Abstract
In feature-level fusion, features extracted from different modalities are fused in order to obtain a single feature set for multimodal biometric recognition systems. These features can be encoded using a binary (1' or '0') encoding technique. The encoded feature value of '1' provides more information about the feature than '0' does. In view of this, we first propose a fusion in order to fuse encoded features obtained from individual feature encoders for a multimodal biometric system, and refer to it as the first-stage fusion (FSF). Next, another fusion is carried out between the unimodal system which provides the best performance in that multimodal system and the proposed FSF, and referred to as the second-stage fusion (SSF). Genuine acceptance rates @4.3% and @4.4% false acceptance rates, and equal error rate are utilized for evaluating the performance of a multi-biometric system using the proposed fusions. Results show that a superior performance is provided by a multi-biometric system using the proposed fusion scheme in comparison with the performance provided by the system using existing fusions or by the unimodal systems.
Year
DOI
Venue
2018
10.1109/NEWCAS.2018.8585495
2018 16th IEEE International New Circuits and Systems Conference (NEWCAS)
Keywords
Field
DocType
Multimodal biometric system,Hash-encoded features,First-stage fusion,Second-stage fusion,Score-level fusion,Feature-level fusion
Pattern recognition,Computer science,Word error rate,Fusion,Feature extraction,Electronic engineering,Artificial intelligence,Hash function,Encoder,Biometrics,Encoding (memory),Binary number
Conference
ISSN
ISBN
Citations 
2472-467X
978-1-5386-4860-5
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
waziha kabir163.18
M. O. Ahmad21157154.87
M. N. Swamy310418.85