基于主分量分析与支持向量机的人脸检测研究

提出一种基于主分量分析和支持向量机的层叠人脸检测算法,用于复杂背景灰度图像的人脸检测。算法首先用主分量分析方法进行粗筛选,滤去大量非人脸窗口,之后用支持向量机对通过的窗口进行分类。由于在通过主分量分析方法所限定的子空间内训练SVM,有效地降低了训练的难度。实验对比数据表明,该方法降低了分类器的训练难度,计算复杂度较低,大大提高了检测速度。

Face Detection Based on Principal Component Analysis and Support Vector Machin

An efficient method of face detection based on Principal Component Analysis( PCA) incorporating with Support Vector Ma- chine ( 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 pro- cess 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.