Abstract
In this paper, a novel method that utilizes feature-level fusion of finger vein (FV) and finger dorsal texture (FDT) images is proposed for human identification. Motivated by Weber’s law, we present $\alpha $ -trimmed Weber representation ( $\alpha $ -TWR) to enhance the foreground lines (FLs), i.e., vessels underneath skin and line-like texture on skin. The proposed $\alpha $ -TWR is robust to illumination variation, as validated by a basic reflective and transmitted imaging model. Cross section asymmetrical coding (CSAC) is performed to extract features for each pixel. The coding value contains discriminative information on the orientation and internal point location of the FLs. The CSAC values of FV and FDT in each point are abreast in terms of binary representation. Local density weighted matching is developed to obtain the matching score between two feature maps. We experimentally show that the proposed method outperforms other unimodal and multimodal identification methods in terms of equal-error-rate.
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