Optimized parallel implementation of face detection based on GPU component

• An algorithm for face detection has been implemented on CPU.
• An acceleration of this algorithm on GPU migration.
• Performance of GPU implementation shows the effectiveness of this implementation.
• Another optimization method on GPU are operated.

Face detection is an important aspect for various domains such as: biometrics, video surveillance and human computer interaction. Generally a generic face processing system includes a face detection, or recognition step, as well as tracking and rendering phase. In this paper, we develop a real-time and robust face detection implementation based on GPU component. Face detection is performed by adapting the Viola and Jones algorithm. We have developed and designed optimized several parallel implementations of these algorithms based on graphics processors GPU using CUDA (Compute Unified Device Architecture) description.First, we implemented the Viola and Jones algorithm in the basic CPU version. The basic application is widened to GPU version using CUDA technology, and freeing CPU to perform other tasks. Then, the face detection algorithm has been optimized for the GPU using a grid topology and shared memory. These programs are compared and the results are presented. Finally, to improve the quality of face detection a second proposition was performed by the implementation of WaldBoost algorithm.

Figure optionsDownload as PowerPoint slide