Roman Kapustiak
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Roman Kapustiak has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Lesia Mochurad
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine
1 shared publication
Latest Publications
- Open Access
- 17 September 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (18) , 10139
Parallel Approaches for SNN-Based Nearest Neighbor Search in High-Dimensional Embedding Spaces: Application to Face Recognition
The rapid growth of high-dimensional biometric data requires fast and accurate similarity search methods for real-time applications. This study proposes, for the first time, two efficient parallel implementations of the exact Sorting-based Nearest Neighbor (SNN) algorithm using OpenMP for CPUs and CUDA for GPUs. Comparative evaluation against conventional exact search methods—k-d tree and ball tree—on LFW embeddings, including FaceNet512 and VGG-Face, demonstrates an up to 58× speedup on GPUs while maintaining full accuracy. Analysis of the full recognition pipeline shows that parallelization reduces search times to about 27% of total processing, highlighting the method’s stability and efficiency for modern embeddings. These results confirm the applicability of the proposed approaches for real-time biometric identification, with potential extensions to streaming data, hybrid computing environments, and other high-dimensional representations.
- 17 September 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (18) , 10139
Open AccessParallel Approaches for SNN-Based Nearest Neighbor Search in High-Dimensional Embedding Spaces: Application to Face Recognition
The rapid growth of high-dimensional biometric data requires fast and accurate similarity search methods for real-time applications. This study proposes, for the first time, two efficient parallel implementations of the exact Sorting-based Nearest Neighbor (SNN) algorithm using OpenMP for CPUs and CUDA for GPUs. Comparative evaluation against conventional exact search methods—k-d tree and ball tree—on LFW embeddings, including FaceNet512 and VGG-Face, demonstrates an up to 58× speedup on GPUs while maintaining full accuracy. Analysis of the full recognition pipeline shows that parallelization reduces search times to about 27% of total processing, highlighting the method’s stability and efficiency for modern embeddings. These results confirm the applicability of the proposed approaches for real-time biometric identification, with potential extensions to streaming data, hybrid computing environments, and other high-dimensional representations.