| As an important research topic of the pattern recognition and machine vision, the human face feature detection technology has been studied widely in the application area such as the face recognition, new human-computer interaction, information security etc. For these applications have the limitation of the real-time, how to accelerate the speed of the face feature detection has always been an important topic. While CUDA (Compute Unified Device Architecture) starts a new area of doing general-purpose computing on GPUs by providing developers a friendly development environment to fully use GPU's computing power, it also makes the high CPU+GPU cooperative computation model possible. So it provides a new implementation scheme of the real-time face feature detection for us.In this paper, we developed a CPU+GPU desktop face feature detection system which uses the high data-parallel computing power and the high internal data bandwidth of GPU to achieve the coarse granularity task level parallelism and the fine granularity data level parallelism, Thus greatly improve the speed of the algorithm to realize the real-time face feature points detection. Specially, the main work of this dissertation includes the reasonable task assignment of the algorithm to take full advantage of GPU and effectively balances the workload between CPU and GPU -- part of the high data parallel and enormous computation in the algorithm will be mapped into the platform of GPU and the remainder will be mapped into the Platform of CPU—and the design of the parallelism of the algorithm which will be mapped into the platform of GPU. Finally, we test and verify the system running on a NVIDIA Gefoce GTX260 graphics card, which achieve the speed of 71ms/f for a BMP image which size is 640*480 and the detection rate of 94%. Our system can meet real-time acquirement of the relevant applications and provide effective support for all kinds of real-time human-computer interaction field based on the desktop system. |
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