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An Efficient Parallel Implementation of Face Detection System Using CUDA

Face detection is a highly efficient component in diverse domains such as security surveillance. Especially, the Viola-Jones algorithm has achieved significant performances in the field of detection face. In the last years, graphics processors have fast become the mainstay to solve the problem of detection face applications and to accelerate data parallel computing. This is due to their flexibility, and in particular, to the single-instruction, multiple-data execution model exploited for streaming processors by a Graphics Processing Unit (GPU). Therefore, in this paper, the researchers develop a robust face detection implementation based on the GPU component. The implementation has been optimized by following up a strategy to use the different memory resources in GPU and the warp scheduler technique, so as to accelerate the access to the memory, with better exploitation of resources proved by GPU. The results display that the suggested method is very important and consumes less execution time compared with the standard implementation and sequential implementation.