Parallel Approaches for SNN-Based Nearest Neighbor Search in High-Dimensional Embedding Spaces: Application to Face Recognition

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Abstract
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.

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