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.
Due to the vulnerable environment, limited recourse and open communication channel, wireless sensor networks (WSNs) are necessary to be protected from various attacks. The key management is an important way to protect the communication in WSNs. In this paper, we present a hierarchical key management scheme (HKMS) which can efficiently enhance the security and survivability for the clustered WSNs. Different from previous works, the HKMS distributes keys based on hop counts and one-way function by the clustered architecture, which not only localizes the key things but also has no overhead. The HKMS provides the session keys among sensors and the cluster key between the cluster head and member nodes. The HKMS dynamically generates different keys based on different hops in different periods which can protect the network from the compromised nodes and reduce the high probability of the common keys without any special sensors (such as the anchor nodes). The security analysis and simulation show the HKMS can prevent several attacks effectively and reduce the energy consumption.
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.
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