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Modular Real-Time Face Detection System

机译:模块化实时人脸检测系统

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摘要

In this paper, a novel system architecture of face detection in possession of modular characteristic is proposed, and the corresponding face detection method is described, to match with the proposed architecture. First of all, the proposed architecture of face detection consists of two modules, namely, the coprocessor module of face detection based on FPGA and target system module, which hopes to implement finial face detection, based on general purpose CPU, and USB bus is used as the communication bridge between the two modules. Secondly, taking the characteristics of FPGA and general purpose CPU into consideration, face detection algorithm can be divided into two layers. The first layer of face detection algorithm based on skin color and eyes' graylevel variation is implemented in the FPGA, and then the corresponding detection results and image are transmitted to the second module by USB bus so as to further detect face using the algorithm combining principle component analysis with support vector machine, which is referred to as the second layer of algorithm. Because the second layer of the algorithms are operations of float-point and loop, it implemented in the general purpose CPU. This architecture enables face detection to be implemented not only in high performance computing platform in possession of USB bus interface, but also in small terminal products and low-end embedded systems, where the performance of processor and the resource of hardware are limited. Actual testing results show that the proposed system architecture can implement real-time face detection for the images with 640 x 480 resolution, and the detection accuracy is about 89%.
机译:本文提出了一种具有模块化特征的新型人脸检测系统体系结构,并描述了与该体系结构相匹配的人脸检测方法。首先,提出的人脸检测架构由两个模块组成,即基于FPGA的人脸检测协处理器模块和目标系统模块,希望通过通用CPU实现最终人脸检测,并使用USB总线作为两个模块之间的通信桥梁。其次,考虑到FPGA和通用CPU的特性,人脸检测算法可以分为两层。在FPGA中实现了基于肤色和眼睛灰度变化的人脸检测算法的第一层,然后将相应的检测结果和图像通过USB总线传输到第二模块,以利用算法组合原理进一步检测人脸支持向量机进行成分分析,被称为算法的第二层。由于算法的第二层是浮点运算和循环运算,因此在通用CPU中实现。这种架构不仅可以在具有USB总线接口的高性能计算平台中实现人脸检测,而且可以在处理器性能和硬件资源受到限制的小型终端产品和低端嵌入式系统中实现人脸检测。实际测试结果表明,所提出的系统架构可以对640 x 480分辨率的图像进行实时人脸检测,检测精度约为89%。

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