基于2DPCA和支持向量机的人脸检测研究

提出一种基于二维主分量(2DPCA)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。算法首先采用2DPCA分析方法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检测。由于在通过2DPCA分析方法的子空间内训练SVM,降低了分类器的训练难度。并且和传统的PCA方法相比,2DPCA直接采用二维图像矩阵表示人脸,进行特征提取,提高了计算效率。实验对比数据表明该算法大大提高了检测速度,降低了虚警率。

Face Detection Based on Two-Dimensional Principal Component Analysis and Support Vector Machine

An efficient method of face detection based on Two-Dimensional Principal Component Analysis(PCA) incorporating with Support Vector Machine(SVM) is proposed in this paper.Firstly,a 2DPCA 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.As opposed to PCA,2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction.The experiment results show that the method can effectively detect faces under complicated background,and the processing time is shorter than using SVM alone.