摘要
提出了一种基于主分量分析(PCA)和支持向量机(SVM)相结合的人脸检测方法。该方法首先利用计算复杂度较低的PCA粗分类器对输入图像遍历检测,滤除大部分非人脸窗口,再由SVM分类器进行精确判断,从而加快了检测过程。实验证明,本方法能够有效的检测出复杂背景下的人脸图像,并且处理时间比单纯使用SVM大大缩短。
An efficient method of face detection based on Principle Component Analysis (PCA) incorporating with Support Vector Machine (SVM) is proposed in this paper. Firstly, a PCA coarse filter with relatively lower computational complexity is applied to the whole input image to filter out most of the non-face, then follows the SVM classifier to make the final decision, so the detection process is speeded up. The experiment results show that the method can effectively detect faces under complicated background, and the processing time is shorter than using SVM alone.
出处
《计算机与数字工程》
2005年第4期56-58,共3页
Computer & Digital Engineering
关键词
主分量分析
支持向量机
人脸检测
Principle Component Analysis, Support Vector Machine, Face Detection
