Realtime Communication Measurement on Web Services in the Fingerprint Machine The problem of fingerprintbased attendance management has been overcome in previous studies. Web Service Implementation (WS) has successfully handled fingerprint machine communication with a webbased monitoring system with a different platform. However, it is not yet known the size of the data transfer rate between the fingerprint machine and the monitoring system. The primary purpose of this research is to test and measure data transfer speed and memory usage. Experiments in this study were carried out by connecting five fingerprint machines with a webbased monitoring system. Node.js is installed on the serverside that is connected to the web service and database. The experiment is repeated; the data transfer speed and memory usage are recorded in the table and averages are calculated. The experimental results in the study showed that after 50 trials it was known that the average time of data transfer was 14.53 ms and the average memory usage was 17.42 Mb. So that communication between fingerprint machines and webbased monitoring systems can be categorized as realtime communication.

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.