Massive Fingervein Identification based on Local Line Binary Pattern under Parallel and Distributed Systems Massive individuals identification is a challenging problem in modern society. Particularly, fingervein recognition is an emerging biometric technique with several advantages, especially in terms of security against forgery. In this paper, we propose a hybrid parallel matching process for fingervein recognition under two different parallel platforms, by using the Local Line Binary Pattern descriptor and Hamming distance. Our proposal aims to reduce the computation time of the matching process for massive individuals identification by using fingervein patterns. Extensive evaluation shows that our approach obtains a high speedup under a multicore platform and a close to linear behavior for a multinode platform. The results with our hybrid parallel system show that it is suitable for realtime individuals identification, achieving a speedup up to 129.98x. To the best of our knowledge, our work is the first implementation of fingervein recognition under a parallel platform, which is the main contribution of this paper.

Multisample Compression of Finger Vein Images using H.265 Video Coding A new videocompression based approach extending traditional biometric sample data compression techniques is evaluated in the context of finger vein recognition. The proposed scheme is implemented in HEVC  H.265 in different settings and compared to (i) compressing each sample individually with JPEG2000 according to ISOIEC 197949:2011 and to (ii) compressing each usersxe2x80x99 data into an individual video file. Compression efficiency and implications on recognition accuracy are determined using 4 recognition schemes and 2 data sets, both based on publicly available data. Results obtained using the proposed approach are fairly stable across different recognition schemes and data sets and indicate a significant improvement over the current state of the art.