Finger vein biometric has received considerable attentions in recent years. However, there is no approved and benchmark finger vein database for researchers to evaluate their algorithms. Furthermore, few public finger vein databases are available online. Aiming to support a benchmark database, in this paper, we introduce a representative finger vein database captured by a portable device, which is named MMCBNU_6000. Our research is novel in four aspects. First, MMCBNU_6000 is established with participation of 100 volunteers, coming from 20 countries. It contains images acquired from different persons with different skin colors. Second, statistical information of the nationality, age, gender, and blood type is recorded for further analysis on finger vein images. Third, similar to the real application, influences from translation, rotation, scale, uneven illumination, scattering, collection posture, finger tissue and finger pressure are taken into account in the imaging process. Fourth, according to the evaluation of average image gray value, image contrast and entropy on the images from the available databases, the acquired images in MMCBNU_6000 have comparable image quality.

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.