基于主分量分析与支持向量机的人脸检测研究

Face Detection Based on Principal Component Analysis and Support Vector Machin

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。

An efficient method of face detection based on Principal 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.