Parallel implementation of eigenface on CUDA
Manik R. Kawale, Yogesh D. Bhadke, Vandana Inamdar · 2014
Face recognition has many real world applications including surveillance and authentication. Due to complex and multidimensional structure of face it requires huge computations therefore fast face recognition is required. One of the most successful appearance based techniques for face recognition is Principal Component Analysis (PCA) which is generally known as eigenface approach. It suffers from the disadvantage of higher computation cost, despite its better recognition rate. With the increase in number of images in training database and also the resolution of images, the computational cost also increases. In this paper, we present a CUDA implementation of eigenface approach for face recognition. The proposed algorithm has shown a 5× speedup in training phase.