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/lp/springer-journals/modular-real-time-face-detection-system-bHOtkP3LUz
- Publisher
- Springer Journals
- Copyright
- Copyright © 2015 by Springer-Verlag Berlin Heidelberg
- Subject
- Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
- ISSN
- 2198-5804
- eISSN
- 2198-5812
- DOI
- 10.1007/s40745-015-0064-6
- Publisher site
- See Article on Publisher Site
Abstract
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 $$\times $$ × 480 resolution, and the detection accuracy is about 89 %.
Journal
Annals of Data Science – Springer Journals
Published: Dec 23, 2015