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Parallel computing of WaveCluster algorithm for face recognition application

机译:WaveCluster算法的并行计算在人脸识别中的应用

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There have been widely applied many research related to face recognition system. The system is commonly used for video surveillance, human and computer interaction, robot navigation, and etc. Along with the utilization of the system, it leads to the need for a faster system response, such as robot navigation or application for public safety. A number of classification algorithms have been applied to face recognition system, but it still has a problem in terms of computing time. In this system, computing time of the classification or feature extraction is an important thing for further concern. Classification algorithm that is suitable for very large databases and efficient in time complexity is WaveCluster. WaveCluster based on wavelet transform able to analyze function at different resolution. To enhance system ability as a real-time system, WaveCluster will be present to be parallel process and implemented on GPU using CUDA. CUDA is a parallel computing architecture that can manage high-performance parallel computing on GPU with large memory bandwidth. The parallelization of WaveCluster algorithm on GPU using CUDA is expected to speed-up the process computing time compared to serial process on CPU. In addition, the system is intended to improve level of accuracy in recognition process of facial images
机译:与面部识别系统有关的许多研究已被广泛应用。该系统通常用于视频监视,人机交互,机器人导航等。随着系统的使用,它导致需要更快的系统响应,例如机器人导航或公共安全应用。许多分类算法已经被应用到面部识别系统,但是在计算时间方面仍然存在问题。在该系统中,分类或特征提取的计算时间是需要进一步关注的重要事情。 WaveCluster是适用于大型数据库且时间复杂度高的分类算法。基于小波变换的WaveCluster能够分析不同分辨率下的功能。为了增强作为实时系统的系统能力,WaveCluster将呈现为并行过程,并使用CUDA在GPU上实现。 CUDA是一种并行计算体系结构,可以在具有大内存带宽的GPU上管理高性能并行计算。与CPU上的串行处理相比,使用CUDA在GPU上对WaveCluster算法进行并行化有望加快处理计算时间。另外,该系统旨在提高面部图像的识别过程中的准确性水平。

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