Comparison of Joblib and Pymp for Parallel Fingerprint Recognition

Fingerprint recognition is a cornerstone technology in security and identification systems, valued for its reliability and uniqueness. As the complexity of fingerprint data increases, efficient computational techniques become crucial to ensure fast and accurate processing. Parallel computing emerges as a promising solution, distributing computational tasks across multiple processors to enhance performance and reduce processing times. This study compares two par- allel computing libraries, Joblib and Pymp, to assess their effectiveness in optimizing fingerprint recognition algorithms. Joblib is renowned for its ease of integration, memory efficiency, and caching support, making it suitable for machine learning tasks and data preprocessing. Pymp, on the other hand, offers a straightforward API for parallelizing loops and managing shared resources, ideal for tasks that require shared memory. Implementing fingerprint recognition processes with both libraries, we measured their performance in terms of execution time, resource utilization, and ease of use. Contrary to expectations, our results show that Pymp surpasses Joblib in speed, even with a moderate dataset of 407 fingerprint images, thanks to its efficient CPU resource management and flexible parallel loop execution.