Multimodal biometric systems based on fingerprint and finger vein modality provide promising features useful for robust and reliable identity verification. In this paper, we present a robust imaging device that can capture both fingerprint and finger vein simultaneously. The presented low-cost sensor employs a single camera followed by both near infrared and visible light sources organized along with the physical structures to capture good quality finger vein and fingerprint samples. We further present a novel finger vein recognition algorithm that explores both the maximum curvature method and Spectral Minutiae Representation (SMR). Extensive experiments are carried out on our newly collected database that comprises of 1500 samples of fingerprint and finger vein from 150 unique fingers corresponding to 41 subjects. Our results demonstrate the efficacy of the proposed sensor with a lowest Equal Error Rate of 0.78%.
The vascular pattern of the finger has emerged as a promising new biometric modality, characterized by very low error rates, good spoofing resistance and user convenience when compared with other existing biometric modalities. In this paper, we present a new sensor design that is not only cost effective but also robust enough to capture the finger vascular (or finger vein) pattern. We evaluate the previously proposed finger vein verification approaches and also propose a new scheme that illustrates its superiority over existing approaches. Extensive experiments are carried out on the newly developed database constructed using our newly designed finger vein imaging sensor that comprises of 1780 finger vein samples corresponding to 89 unique fingers from 32 subjects. The data is collected in two different scenarios shows the efficacy of the proposed sensor as well as the proposed scheme.
With ever more IoT (Internet of Things) and big data applications, the emerging blockchain techniques provide fundamental supports to credibly track the transactions of digital assets. Public blockchains, e.g., bitcoin, are often energyconsuming and low efficient. Therefore, an empirical study of operating permissioned blockchains in clouds is urgently needed. In this paper, we study the performance of Sawtooth, a wellknown permissioned blockchain platforms from Hyperledger, in cloud environments. Our results provide insights for blockchain operators to optimize the performance of Sawtooth through adjusting the two configuration parameters, i.e., Scheduler and Maximum Batches Per Block. Our approach can be used to test other blockchain platforms.
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