Biblio

Face recognition is a fast-expanding field of research. Countless classification algorithms have found use in face recognition, with more still being developed, searching for better performance and accuracy. For high-dimensional data such as images, the K-Nearest-Neighbours classifier is a tempting choice. However, it is very computationally-intensive, as it has to perform calculations on all items in the stored dataset for each classification it makes. Fortunately, there is a way to speed up the process by performing some of the calculations in parallel. We propose a parallel CUDA implementation of the KNN classifier and then compare it to a serial implementation to demonstrate its performance superiority.