A Feature-Level Fusion Scheme Based on Eigen Theory for Multimodal Biometrics
Wen‐Shiung Chen, Ren-He Jeng, Yen-Feng Chen · IETE Technical Review · 2021
Multimodality is a promising trend to gain the reliability for biometrics. This paper presents a novel multimodal biometric recognition with information fusion at feature level, called eigen-based feature-level fusion (E-FLF) scheme, for personal authentication. The kernel idea of the proposed E-FLF scheme is to perform the eigen analysis for finding an optimum projection on the feature's cross-energy space by maximizing cross-energy ratio in the new projection space. Simple local and global features extracted from multiple biometric modalities, such as iris, palmprint and face, are considered in this fusion scheme. Different modes of fusion have been implemented to verify the validation of the proposed method. Experimental results reveal that the proposed fusion scheme for fusing features from multimodal biometric traits may improve the recognition performance significantly.