GPU and CPU Cooperative Accelaration for Face Detection on Modern Processors

Along with the inclusion of GPU cores within the same CPU die, the performance of Intel's processor-graphics has been significantly improved over earlier generation of integrated graphics. The need to efficiently harness the computational power of the GPU in the same CPU die is more than ever. This paper presents a highly optimized Haar-based face detector which efficiently exploits both CPU and GPU computing power on the latest Sandy Bridge processor. The classification procedure of Haar-based cascade detector is partitioned to two phases in order to leverage both thread level and data level parallelism in the GPU. The image downscaling and integral image calculation running in the CPU core can work with the GPU in parallel. Compared to CPU-alone implementation, the experiments show that our proposed GPU accelerated implementation achieves a 3.07x speedup with more than 50% power reduction on the latest Sandy Bridge processor. On the other hand, our implementation is also more efficient than the CUDA implementation on the NVidia GT430 card in terms of performance as well as power. In addition, our proposed method presents a general approach for task partitioning between CPU and GPU, thus being beneficial not only for face detection but also for other multimedia and computer vision techniques.