In our previous work [10], an effective method based on minutiae feature matching was proposed for finger-vein recognition. To further improve performance, a region growth-based feature extraction method [11] is employed to extract the vein patterns from unclear images. For a small database, the two methods can achieve high accuracy by matching these images. Currently, a wide line detector is being investigated for finger-vein feature extraction by Huang et al. [12]. Their experimental results have shown that a wide line detector combined with pattern normalization can obtain the best results among these methods. Meanwhile, a new finger-vein extraction method using the mean curvature [13] is developed to extract the pattern from the images with unclear veins.

As the mean curvature is a function of the location and does not depend on the direction, it achieves better performance than other methods.The vein feature extraction methods described above have shown better performance for finger-vein recognition, however, they have the following limitations: (1) as some of the pattern extraction methods such as maximum curvature [6] and mean curvatures [13] emphasize the pixel curvature, the noise and irregular shading are easily enhanced. Thus, they cannot detect effective vein patterns for authentication; (2) The methods described above only focus on single feature extraction (the shape of veins), rather than multi-feature extraction.

However, it is difficult to extract a robust vein pattern because the captured vein images contain irregular shading and noise, therefore, only by using the shape of vein patterns one cannot achieve robust performance in finger-vein recognition; (3) The matching scores generated from these methods are either global or local, so it is difficult to accommodate the local and global changes at the same time. To solve these problems, a new scheme is proposed herein for finger-vein recognition. The main contributions from this paper can be summarized as follows:Firstly, this paper proposes a new approach which can extract two different types of finger-vein features and achieves a most promising performance. Unlike the existing approaches based on curvature [6,13], the proposed method emphasizes the difference value of the two curvatures in any two orthogonal tangential directions, so the finger region vein can be distinguished from other regions such as the flat region, the isolated noise and irregular shading.

Meanwhile, the finger-vein orientation is also estimated by computing Batimastat the maximum difference value.Secondly, we proposed a localized matching method to accommodate the potential local and global variations at same time. The localized vein sub-regions are obtained according to feature points which can be determined by the improved feature points removal scheme in the SIFT framework.