A First Step to Accelerating Fingerprint Matching Based on Deformable Minutiae Clustering

Abstract

Fingerprint recognition is one of the most used biometric methods for authentication. The identification of a query fingerprint requires matching its minutiae against every minutiae of all the fingerprints of the database. The state-of-the-art matching algorithms are costly, from a computational point of view, and inefficient on large datasets. In this work, we include faster methods to accelerating DMC (the most accurate fingerprint matching algorithm based only on minutiae). In particular, we translate into C++ the functions of the algorithm which represent the most costly tasks of the code; we create a library with the new code and we link the library to the original C# code using a CLR Class Library project by means of a C++/CLI Wrapper. Our solution re-implements critical functions, e.g., the bit population count including a fast C++ PopCount library and the use of the squared Euclidean distance for calculating the minutiae neighborhood. The experimental results show a significant reduction of the execution time in the optimized functions of the matching algorithm. Finally, a novel approach to improve the matching algorithm, considering cache memory blocking and parallel data processing, is presented as future work.

Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

“…It allows us to test the performance of ALFI on Linux and Windows operating systems (OSs) using the same implementation. We have proven that its use significantly improves the computational performance of the latent identification task according to a previous research presented in [41].…”

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

“…The speed of validation on each gesture data is up to 2 ms. Though it is slower than fingerprint validation, which can be validated in less than 100 µs [35,36], it can be very similar to face recognition, with recognition speed of 2.4 ms [37]. In normal use, it is fast enough to be indiscernible by users.…”