Multimodal biometric recognition has been widely used in identity authentication. However, how to fuse the multimodal images together reliably and effectively is still a challenging problem in practice. In this paper, combining multimodal traits, fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP), as a global representation of a finger, a new pixel-based granular fusion method is proposed. In the proposed method, each unimodal image is first viewed as an atomic hypersphere granule with a center denoted by a real N-dimensional pixel-value vector. Thus, for a finger trait, a triangle can be constituted by the centers corresponding to three atomic granules such that an inscribed circle of it can be formed subsequently. A fused hypersphere granule of a finger is therefore generated coordinately by combing centers of the FV granule and the inscribed circle. Finally, the fuzzy inclusion measure is used to compute the similarity between two fusion hypersphere granules for image matching. Experiment results show that the proposed granular fusion method at pixel level is reliable and effective.
Personal identification based on single-spectral finger-vein image has been widely investigated recently. However, in finger-vein imaging, finger-vein image degradation is the main factor causing lower recognition accuracy. So, to improve the finger-vein image quality, in this paper, multispectral finger-vein images (760nm and 850nm) are fused together for contrast enhancement using NSCT transformation. The proposed method can preserve the completeness and sharpness of finger-vein. Experimental results demonstrate that the proposed method is certainly powerful in enhancing finger-vein image contrast and achieves lower equal error rates in finger-vein recognition even if original images have poor contrast.
Multimodal biometrics based on the finger identification is a hot topic in recent years. In this paper, a novel fingerprint-vein based biometric method is proposed to improve the reliability and accuracy of the finger recognition system. First, the second order steerable filters are used here to enhance and extract the minutiae features of the fingerprint (FP) and finger-vein (FV). Second, the texture features of fingerprint and finger-vein are extracted by a bank of Gabor filter. Third, a new triangle-region fusion method is proposed to integrate all the fingerprint and finger-vein features in feature-level. Thus, the fusion features contain both the finger texture-information and the minutiae triangular geometry structure. Finally, experimental results performed on the self-constructed finger-vein and fingerprint databases are shown that the proposed method is reliable and precise in personal identification.