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.