摘要
人脸识别技术拥有广泛的应用前景,但是目前不少实现方式存在一些不尽人意之处。在对OPENCV与SVM分类器进行分析的基础上,阐述了基于两级分类器的人脸检测方法的原理和实现过程,首先分析了两级分类器的构建,引入人脸图像的矩形特征向量,将图像的矩形特征作为分类的依据,随后论述了系统设计与实现,包括灰度变换过程、直方图均衡过程、图像平滑过程以及金字塔序列化的实现。这种检测模式能够加快处理速度,提升效率。
The face recognition technology has broad application prospects, but right now there are some failings in many implementations. Based on the analysis of OPENCV with the SVM classifier, this paper elaborated a strategy based on two-stage classifier of face detection principle and the implementation process, first, analysed two classifier construction,introduced a rectangular face image feature vector,used the image of the rectangular features as the basis for classification, and then discussed the system design and implementation, including the gray-scale transformation process, the process of histogram equalization, image smoothing serialization process as well as the realization of the pyramid. This test model can speed up the processing speed and has higher efficiency.
出处
《计算机科学》
CSCD
北大核心
2010年第4期293-295,298,共4页
Computer Science
基金
甘肃省自然科学基金(0809RJZA015
智能化的混合元搜索引擎研究)资助
